CN117897090A

CN117897090A - System and method for sensing bowel movement events

Info

Publication number: CN117897090A
Application number: CN202280059512.8A
Authority: CN
Inventors: N·V·安内塔; M·J·阿什; C·W·贝克; U·E·巴鲁克; J·R·布兰奇; 党项南; Y·N·戴维斯; M·费尔南德斯-马托斯巴尔森; M·K·福特汉姆; C·E·福特尼; C·M·芬克豪瑟; K·T·戈特利布; A·C·哈特; S·E·哈卡比; I·库尔蒂斯; L·库尔蒂斯; S·M·库特; C·S·兰哈姆; E·C·迈耶斯; P·J·欧文
Original assignee: Eli Lilly and Co
Current assignee: Eli Lilly and Co
Priority date: 2021-09-03
Filing date: 2022-09-01
Publication date: 2024-04-16

Abstract

Systems and methods facilitate sensing and counting bowel movement events in a subject, such as a participant in a clinical trial for treating digestive system diseases such as Irritable Bowel Syndrome (IBS), inflammatory Bowel Disease (IBD), and chronic constipation. The systems and methods may also be used by individual patients for sensing and counting bowel movement events, and the resulting data may be reviewed by a healthcare provider when assessing patient gastrointestinal health and/or treatment.

Description

System and method for sensing bowel movement events

Technical Field

The present disclosure relates to systems and methods for automatically sensing a bowel movement event of a subject. More particularly, the present disclosure relates to systems and methods for sensing bowel movement events by sensing one or more actions associated with such events.

Background

For diagnosis, assessment, treatment and/or management of various digestive diseases, such as Irritable Bowel Syndrome (IBS), inflammatory Bowel Disease (IBD) and chronic constipation, accurate acquisition of bowel event data (e.g., frequency and/or timing of such events) is typically required. However, obtaining such data can be cumbersome and typically relies on patient recall, which can lead to inaccuracy.

Disclosure of Invention

The present disclosure provides systems and methods for easily and accurately sensing a bowel movement event and obtaining bowel movement event data. These systems and methods do not require patient recall to be relied upon.

In accordance with an embodiment of the present disclosure, a system for sensing a bowel movement event of a subject includes a wearable device configured to be carried on a torso of the subject. The wearable device is operable in a sleep mode and an active mode. The wearable device includes a wake-up sensor configured to sense a first stimulus and a Mechanical Myogram (MMG) sensor configured to sense an abdominal muscle movement signal of a subject. The processor is operably coupled to the wake-up sensor and the MMG sensor. The processor reconfigures the wearable device from the sleep mode to the active mode based on the first stimulus sensed by the wake sensor. In the active mode, the wearable device is configured to communicate with the processor to determine an occurrence of a bowel movement event of the subject based on the abdominal muscle movement signal sensed by the MMG sensor.

In some embodiments, the abdominal muscle movement signal of the subject is a second stimulus, and the system further comprises a third sensor operatively coupled to the processor and configured to sense the third stimulus. In the active mode, the processor is configured to determine an occurrence of a bowel movement event of the subject based on the abdominal muscle movement signal sensed by the mechanical muscle map sensor and the third stimulus sensed by the third sensor.

In some embodiments, the third sensor includes a gas sensor disposed in the wearable device and configured to sense flatus.

In some embodiments, the third sensor is an audio sensor configured to sense a toilet flushing sound. In some embodiments, the audio sensor may also be configured to sense sounds caused by or associated with movement of the garment, such as sand sounds of the lower body garment when it is removed.

In some embodiments, the third sensor is an electromyographic electrode configured to sense an electrical muscle signal of the subject.

In some embodiments, the third sensor is an inertial measurement unit configured to sense a change in posture of the subject.

In some embodiments, the wearable device further comprises a patch configured to be carried on the torso of the subject, and the patch carries the wake-up sensor and the mechanical myogram sensor.

In some embodiments, the patch further carries a processor.

In some embodiments, the wearable device further comprises a waistband configured to extend around the torso of the subject, and the waistband carries the wake-up sensor and the mechanical myogram sensor.

In some embodiments, the waistband further carries a processor.

In some embodiments, the wake-up sensor comprises one of an optical sensor and a resistance sensor configured to sense when the subject removes lower body clothing.

In some embodiments, the wearable device further comprises a health sensor configured to sense a health stimulus associated with the health of the subject.

In some embodiments, the health sensor comprises a blood sensor configured to sense blood in the stool of the subject.

In some embodiments, the blood sensor includes a solid state vapor detection sensor configured to sense one or more volatile organic compounds.

According to another embodiment of the present disclosure, a system for sensing a bowel movement event of a subject includes a wearable device configured to be carried on a torso of the subject. The wearable device includes a mechanical myograph sensor configured to sense an abdominal muscle movement signal of a subject and a gas sensor configured to sense flatus. The processor is operably coupled to the mechanical myograph sensor and the gas sensor. The processor is configured to determine an occurrence of a bowel movement event of the subject based on the abdominal muscle movement signal sensed by the mechanical myogram sensor and the flatus sensed by the gas sensor.

In some embodiments, the processor is configured to determine occurrence of a bowel movement event of the subject based on a sequence of events including one of an abdominal muscle movement signal sensed by the mechanical myogram sensor and flatus sensed by the gas sensor preceding the other of the abdominal muscle movement signal sensed by the mechanical myogram sensor and the flatus sensed by the gas sensor.

In some embodiments, the wearable device further comprises a base carrying the mechanical-actuation-map sensor, the gas sensor, and the processor.

According to yet another embodiment of the present disclosure, a system for sensing a bowel movement event of a subject includes a wearable device configured to be carried on a torso of the subject and under lower body clothing worn by the subject. The wearable device includes an optical sensor configured to sense increased light when the subject removes lower body clothing. The processor is operably coupled to the optical sensor and is configured to determine an occurrence of a bowel movement event based at least in part on the increased light sensed by the optical sensor.

In some embodiments, the wearable device is operable in an active mode and a sleep mode, and the processor reconfigures the wearable device from the sleep mode to the active mode upon determining that the subject removes the lower body clothing in response to the increased light sensed by the optical sensor.

In some embodiments, the optical sensor is a first sensor configured to sense light as the first stimulus. The wearable device further includes a second sensor configured to sense a second stimulus when the wearable device is in an active mode. The second stimulus is different from the first stimulus. The processor is operably coupled to the second sensor and the processor is configured to determine an occurrence of a bowel movement event of the subject based on the signal received from the second sensor.

In some embodiments, the optical sensor is a first sensor configured to sense light as the first stimulus. The wearable device further includes a second sensor configured to sense a second stimulus when the wearable device is in an active mode. The second stimulus is different from the first stimulus. The processor is operably coupled to the second sensor and the processor is configured to determine an occurrence of a bowel movement event of the subject based on signals received from the first sensor and the second sensor.

In some embodiments, the second sensor is a mechanical myograph sensor.

In some embodiments, the wearable device further comprises a patch configured to be carried on the torso of the subject, and the patch carries the optical sensor and the processor.

In some embodiments, the wearable device further comprises a waistband configured to extend around the torso of the subject, the waistband carrying the optical sensor and the processor.

According to yet another embodiment of the present disclosure, a method for sensing a bowel movement event of a subject includes: sensing, by an optical sensor of a wearable device carried on a torso of a subject, increased light when the subject removes lower body clothing; sensing, by a mechanical myogram sensor of the wearable device, an abdominal muscle movement signal of the subject; and determining an occurrence of a bowel movement event based at least in part on the increased light sensed when the subject removes the lower body clothing and the sensed abdominal muscle movement signal of the subject.

In some embodiments, the method further comprises sensing, by a gas sensor of the wearable device, flatus, and the determination that the bowel movement event has occurred is based at least in part on the sensed flatus.

In some embodiments, the method further comprises sensing, by the inertial measurement unit of the wearable device, a sitting motion of the subject prior to sensing the abdominal muscle movement signal of the subject, and determining that the bowel movement event has occurred is based at least in part on the sensed sitting motion.

In some embodiments, the method further comprises, after sensing flatus, sensing, by an inertial measurement unit of the wearable device, a standing motion of the subject, and determining that a bowel movement event has occurred is based at least in part on the sensed standing motion.

In some embodiments, the method further comprises sensing a plurality of bowel movements events of the subject over a period of time.

In some embodiments, sensing each of a plurality of bowel movements events of the subject comprises: sensing, by a mechanical myogram sensor of the wearable device, an abdominal muscle movement signal of the subject; sensing, by the optical sensor, increased light when the subject removes lower body clothing; and determining an occurrence of each of the plurality of bowel movement events based at least in part on the sensed abdominal muscle movement signal of the subject and the increased light sensed when the subject removes the lower body clothing.

In some embodiments, sensing the increased light by the optical sensor when the subject removes the lower body clothing precedes sensing the abdominal muscle movement signal of the subject by the mechanical myogram sensor.

In some embodiments, sensing the abdominal muscle movement signal of the subject by the mechanical myogram sensor precedes sensing, by the optical sensor, the increased light when the subject removes the lower body clothing.

In some embodiments, the method further comprises reconfiguring the wearable device from a sleep mode to an active mode based on the increased light sensed when the subject removes the lower body clothing, in the sleep mode the mechanical actin sensor is inactive, and in the active mode the mechanical actin sensor is configured to sense an abdominal muscle movement signal of the subject.

According to yet another embodiment of the present disclosure, a system for training one or more processors to detect a bowel movement event of a subject includes a first wearable device configured to be carried on a body of the subject. The first wearable device includes a first bowel movement event sensor configured to sense one or more first stimuli. The system further includes a second wearable device configured to be carried on the body of the subject. The second wearable device includes a second bowel movement event sensor configured to sense one or more second stimuli. The system further includes one or more processors operatively coupled to the first and second bowel movement event sensors and configured to determine an occurrence of a bowel movement event of the detected subject based on the one or more second stimuli and to associate the first stimulus sensed by the first bowel movement event sensor with the bowel movement event of the subject within a predetermined period of the detected bowel movement event.

In some embodiments, the one or more processors are further configured to train a machine learning algorithm for detecting a bowel movement event of the subject using data indicative of a first stimulus sensed by the first bowel movement event sensor that has been associated with the bowel movement event of the subject.

In some embodiments, the first wearable device further comprises a wake sensor configured to sense one or more wake stimuli. The first wearable device is configured to transition from a sleep mode to an active mode when the wake sensor senses a wake stimulus associated with a bowel movement event of the subject, in which the first wearable device is configured to sense a bowel movement event of the subject via the first bowel movement event sensor. The one or more processors are further configured to associate a wake stimulus sensed by the wake sensor during a second predetermined time period of the detected bowel movement event with the bowel movement event of the subject.

In some embodiments, the one or more processors include a first processor operatively coupled to the first bowel movement event sensor and a second processor operatively coupled to the second bowel movement event sensor, wherein the first processor and the second processor are operatively coupled to each other.

In some embodiments, the one or more processors are comprised of a single processor operatively coupled to the first and second bowel movement event sensors.

In some embodiments, the first wearable device comprises a smart watch.

In some embodiments, the second wearable device includes a patch configured to be carried on a torso of the subject.

In some embodiments, the first wearable device includes at least one of the one or more processors.

In some embodiments, at least one of the one or more processors is in wireless communication with the first wearable device.

In some embodiments, the first stimulus and the second stimulus are different types of stimulus.

In some embodiments, the first stimulus and the second stimulus are the same type of stimulus.

According to another embodiment of the present disclosure, a method for training one or more processors operatively coupled to a first wearable device to detect a bowel movement event of a subject includes: sensing, by a first bowel movement event sensor carried by a first wearable device, one or more first stimuli; sensing, by a second bowel movement event sensor carried by a second wearable device, one or more second stimuli; determining, by the one or more processors, occurrence of a bowel movement event of the detected subject based on the second stimulus; and associating, by the one or more processors, the first stimulus sensed by the first bowel movement event sensor with a bowel movement event of the subject within a predetermined period of time of the detected bowel movement event.

In some embodiments, the method further comprises training, by the one or more processors, a machine learning algorithm for detecting a bowel movement event of the subject using data indicative of a first stimulus sensed by the first bowel movement event sensor that has been associated with the bowel movement event of the subject.

In some embodiments, the first wearable device further comprises a wake sensor configured to sense one or more wake stimuli, the first wearable device being configured to transition from a sleep mode to an active mode when the wake sensor senses a wake stimulus associated with a bowel movement event of the subject, in the active mode, the first wearable device being configured to sense the bowel movement event of the subject via the first bowel movement event sensor. The method further includes associating, by the one or more processors, a wake stimulus sensed by a wake sensor during a second predetermined period of the detected bowel movement event with the bowel movement event of the subject. The second predetermined period of time may be the same as or different from the predetermined period of time.

According to another embodiment of the present disclosure, a system for training one or more processors to detect a bowel movement event of a subject includes a wearable device configured to be carried on the body of the subject. The wearable device includes a bowel movement event sensor configured to sense one or more stimuli. The system further includes a mobile device configured to receive user input from the subject indicating a bowel movement time point at which a bowel movement event occurred. The system further includes one or more processors operatively coupled with the bowel movement event sensor and the mobile device, the mobile device configured to correlate the stimulus sensed by the bowel movement event sensor with a bowel movement event of the subject over a predetermined period of time at a point in time of the bowel movement.

In some embodiments, the wearable device further comprises a wake sensor configured to sense one or more wake stimuli, wherein the wearable device is configured to transition from a sleep mode to an active mode when the wake sensor senses a wake stimulus associated with a bowel movement event of the subject, in which active mode the wearable device is configured to sense a bowel movement event of the subject via the bowel movement event sensor. The one or more processors are further configured to associate a wake stimulus sensed by the wake sensor during a second predetermined time period of the detected bowel movement event with the bowel movement event of the subject. The second predetermined period of time may be the same as or different from the predetermined period of time.

According to yet another embodiment of the present disclosure, a method for training one or more processors operatively coupled to a wearable device to detect a bowel movement event of a subject includes sensing one or more stimuli by a bowel movement event sensor carried by the wearable device. The method further includes receiving, via the mobile device of the subject, user input indicating a bowel movement time point at which a bowel movement event occurred. The method further includes associating, by the one or more processors, the stimulus sensed by the bowel movement event sensor with a bowel movement event of the subject within a predetermined period of time at a point in time of the bowel movement.

In some embodiments, the wearable device further comprises a wake sensor configured to sense one or more wake stimuli, the wearable device configured to transition from a sleep mode to an active mode when the wake sensor senses a wake stimulus associated with a bowel movement event of the subject, the method further comprising associating, by the one or more processors, the wake stimulus sensed by the wake sensor with the bowel movement event of the subject within a second predetermined period of the detected bowel movement event.

Drawings

The above-mentioned and other advantages and objects of this invention, and the manner of attaining them, will become more apparent and the invention itself will be better understood by reference to the following description of an embodiment of the invention taken in conjunction with the accompanying drawings, wherein:

fig. 1 is a schematic representation of a system for sensing a bowel movement event of a subject according to an embodiment of the present disclosure.

Fig. 2 is a perspective view of a wearable device for sensing a bowel movement event of a subject according to another embodiment of the present disclosure.

Fig. 3 is a bottom view of the wearable device of fig. 2.

Fig. 4A and 4B are front and rear perspective views, respectively, of a wearable device for sensing a bowel movement event of a subject according to another embodiment of the present disclosure.

Fig. 5 is a perspective view of a wearable device for sensing a bowel movement event of a subject according to yet another embodiment of the present disclosure.

Fig. 6 is a perspective view of a wearable device for sensing a bowel movement event of a subject according to further embodiments of the present disclosure.

Fig. 7 is a top view of the wearable device of fig. 6.

Fig. 8 is a bottom view of the wearable device of fig. 6.

Fig. 9 is a flowchart of a method for sensing a bowel movement event of a subject according to an embodiment of the present disclosure.

Fig. 10-16 illustrate actions associated with another method for sensing a bowel movement event of a subject in accordance with embodiments of the present disclosure.

Fig. 17 is a flowchart of a method for training a first wearable device to sense a bowel movement event by using a second wearable device, in accordance with an embodiment of the present disclosure.

Fig. 18 is a flowchart of a method for training a wearable device to sense a bowel movement event using manual user input received via a mobile device of a subject, according to an embodiment of the present disclosure.

Corresponding reference characters indicate corresponding parts throughout the several views. Although the drawings represent embodiments of the present invention, the drawings are not necessarily to scale and certain features may be exaggerated or omitted in some of the drawings in order to better illustrate and explain the present invention.

Detailed Description

Systems and methods according to embodiments of the present disclosure facilitate sensing a bowel movement event of a subject by sensing one or more stimuli associated with the bowel movement event. The subject may be enrolled in a clinical trial for treating gastrointestinal disorders such as Irritable Bowel Syndrome (IBS), inflammatory Bowel Disease (IBD) and chronic constipation. Alternatively, systems and methods according to embodiments of the present disclosure may be used by individual patients for sensing and counting bowel movement events, and the resulting data may be reviewed by a healthcare provider when assessing patient gastrointestinal health and/or treatment.

Referring now to fig. 1, a system 100 for sensing a bowel movement event of a subject in accordance with an embodiment of the present disclosure is schematically illustrated. In general, the system 100 includes a wearable device 102, the wearable device 102 configured to be worn by, attached to, or otherwise carried by a subject. The wearable device 102 senses one or more stimuli indicative of a bowel movement event of the subject. The wearable device 102 is operatively coupled to one or more remote devices 104 (illustratively, via wireless communication-as used herein, the term "operatively coupled" includes both wired data communication and wireless data communication, whether direct or indirect via one or more intermediary devices or components, and such data communication may be continuous or intermittent), and the wearable device 102 transmits data regarding the bowel movement event(s) to the remote device(s) 104. The remote device 104 may analyze, display, or otherwise provide data regarding the bowel movement event to one or more users, such as a clinical trial manager, a healthcare provider, or the subject herself/himself. These aspects are described in further detail below.

With continued reference to fig. 1, the wearable device 102 includes a base 106, the base 106 configured to be attached to or otherwise carried by a subject. The base 106 carries electronics 108, which electronics 108 facilitate sensing and counting bowel movement events of the subject. In the illustrated embodiment, the electronic component 108 includes one or more wake sensors 110, the one or more wake sensors 110 sensing a "wake" stimulus prior to a bowel movement event. The wake-up sensor(s) 110 are operably coupled to the processor 112 (illustratively, via wired communication). When wake-up sensor(s) 110 sense one or more wake-up stimulus, processor 112 reconfigures device 102 from the sleep mode to the active mode. In the sleep mode, one or more components of the electronic assembly 108 may operate in a low power consumption state (e.g., at a lower clock rate, a lower voltage, or both), or be inactive or "off. In some embodiments, in sleep mode, the processor 112 may be in a state in which signals output from the sensors are ignored, not processed, or not stored. In the active mode, one or more components of the electronic assembly 108 may operate in a high power consumption state (e.g., at a higher clock rate, a higher voltage, or both), or be active or "on". Illustratively, such components may include one or more bowel movement event sensors 114 that sense stimulus during a bowel movement event. In some embodiments, in the active mode, the processor 112 may enter a state in which signals output from the sensors are processed and/or stored. In other words, the wake sensor(s) 110 may activate the device 102 for potential bowel movement events, and the bowel movement event sensor 114 may then be used to confirm the occurrence of a bowel movement event.

The wake-up sensor(s) 110 may take various forms. For example, wake-up sensor(s) 110 may include one or more optical sensors and/or one or more resistance sensors that sense when the subject removes lower body clothing. More specifically, the optical sensor(s) sense increased light when the subject removes the lower body clothing, and the resistance sensor(s) sense insufficient contact force applied by the lower body clothing. As another example, wake-up sensor(s) 110 may include one or more audio sensors that sense one or more corresponding sounds when the subject removes lower body clothing. As yet another example, wake sensor(s) 110 may include one or more accelerometers and/or Inertial Measurement Units (IMUs) that detect changes in user gestures, e.g., associated with sitting down or standing up. The wake stimulus sensed by the wake sensor and causing the electronic component 108 to switch from the inactive state to the active state may include a single sensed stimulus, such as increased light, removal of contact force from the resistance sensor, and/or sound associated with the removal of clothing. Alternatively or additionally, the wake-up stimulus may include a plurality of stimuli (e.g., any of the previously mentioned stimuli) sensed in a particular sequence, or a plurality of stimuli grouped in temporal proximity. Specific embodiments of wearable devices including such wake-up sensors are described in further detail below.

Similarly, the bowel movement event sensor(s) 114 may take various forms. For example, the bowel movement event sensor(s) 114 may include one or more Electromyography (EMG) electrodes for sensing abdominal muscle electrical signals of the subject, which may be indicative of the contraction of those muscles during a bowel movement event (e.g., during valsalva maneuver). As another example, the bowel movement event sensor(s) 114 may include one or more Electrocardiogram (ECG) electrodes and/or one or more photoplethysmography (PPG) sensors for sensing an electrocardiographic signal of the subject, which may be indicative of a decrease and subsequent increase in heart rate during valsalva maneuver. As another example, the bowel movement event sensor(s) 114 may include one or more Mechanical Myogram (MMG) sensors for sensing low frequency vibrations of the subject's abdominal muscles, which may be indicative of contraction of those muscles during a bowel movement event. As another example, the bowel movement event sensor(s) 114 may include one or more Inertial Measurement Units (IMUs) for sensing changes in the posture of the subject, more particularly, changes in the sitting posture prior to a bowel movement event and/or changes in the standing posture after a bowel movement event. As another example, the bowel movement event sensor(s) 114 may include one or more audio sensors for sensing sounds emanating from the intestines of the subject, toilet flushing sounds, and/or sounds associated with the gastrointestinal tract. As another example, the bowel movement event sensor(s) 114 may include one or more gas sensors configured to sense flatus. As another example, the bowel movement event sensor(s) 114 may include one or more temperature sensors for sensing changes in bowel temperature that may occur before or during a bowel movement event. As another example, the bowel movement event sensor(s) 114 may include one or more optical sensors and/or one or more resistance sensors that sense when the subject removes lower body clothing. Specific embodiments of wearable devices including such a bowel movement event sensor are described in further detail below.

With continued reference to fig. 1, in the illustrated embodiment, the electronic assembly 108 further includes one or more health sensors 115 that sense stimuli associated with the health of the subject. The health sensor(s) 115 may take various forms. For example, the health sensor 115 may be a blood sensor that detects blood in a subject's stool, which may be indicative of one or more gastrointestinal disorders. More specifically, the blood sensor may be a solid vapor detection sensor that detects one or more Volatile Organic Compounds (VOCs) (e.g., hexanal, heptanal, octanal, nonanal, decanal, and/or 1-octen-3-one, formed by the reaction of ions in blood and lipids in stool) that create a "metallic odor. In some embodiments, the health sensor(s) 115 may be generally in a sleep mode, and the health sensor(s) 115 may be reconfigured to an active mode when one or more of the bowel movement event sensors 114 detect a bowel movement event. Alternatively, the health sensor(s) 115 may be generally in a sleep mode, and the health sensor(s) 115 may be reconfigured to an active mode when one or more of the wake sensors 110 sense one or more wake stimuli.

With further reference to fig. 1, the processor 112 may be any device or component capable of executing stored software and/or firmware code that, when executed by the processor 112, causes the wearable device 102 to perform the functions described herein. The processor 112 may be, for example, an Application Specific Integrated Circuit (ASIC), a Field Programmable Gate Array (FPGA), a Digital Signal Processor (DSP), hardwired logic, a combination thereof, or the like.

The processor 112 is operably coupled to the memory 116 (illustratively, via wired communication) for storing data regarding bowel movement events and/or subject health. Such data may include, for example, a bowel movement event time, an event length, wake sensor(s) 110 and/or bowel movement event sensor 114 that sensed an event, wake sensor(s) 110 and/or bowel movement event sensor 114 that did not sense an event, data received from health sensor(s) 115, and the like. Memory 116 may be any suitable computer-readable medium accessible by processor 112. The memory 116 may be a single storage device or multiple storage devices, may be internal or external to the processor 112, and may include both volatile and nonvolatile media. The memory 116 may be, for example, random Access Memory (RAM), read Only Memory (ROM), electrically Erasable Programmable ROM (EEPROM), flash memory, magnetic storage devices, optical disk storage or any other suitable medium capable of storing data and accessible by the processor 112.

The processor 112 is also operably coupled to a power source 118 (illustratively, via wired communication) for providing power to various components of the electronic assembly 108, including the wake sensor(s) 110, the bowel movement event sensor(s) 114, and the health sensor(s) 115. The power source 118 may be, for example, one or more rechargeable batteries, one or more inductive/wireless power receivers, or the like.

With continued reference to fig. 1, the processor 112 is also operatively coupled to a user interface 122 (illustratively, via wired communication). The user interface 122 is operable to receive one or more user inputs (e.g., manually confirm the occurrence of a bowel movement event) and/or display data, information, and/or prompts generated by the system 100. The user interface 122 may include at least one input device for receiving user input. The user interface 122 may include a Graphical User Interface (GUI) with a touch screen display operable to display data and receive user inputs. Alternatively, the user interface 122 may include a non-touch screen display, a keyboard, a keypad, a microphone, a speaker, combinations thereof, and the like.

The processor 112 is further operably coupled to a transmitter 120 (illustratively via wired communication) for wireless transmission of information, such as bowel movement event information and/or subject health data stored by the memory 116, to the remote device(s) 104. The transmitter 120 may be, for example, a bluetooth transmitter, an IEEE 802.11 transmitter, a cellular communication transmitter, or a near field communication transmitter, etc. Transmitter 120 may be continuously coupled to or intermittently coupled to remote device(s) 104. Instead of transmitter 120, a transceiver (not shown) may be used to facilitate providing information from remote device(s) 104 to wearable device 102. Such information may include, for example, software updates.

The remote device(s) 104 may be, for example, a mobile device such as a smart phone, a smart watch or tablet device, a personal computer, a remote computer or database, or the like. In a clinical trial setting, remote device(s) 104 may be able to analyze bowel movement event data and/or subject health data received from various wearable devices 102 and evaluate the efficacy of one or more treatments provided to the subject using wearable device 102. In other settings, remote device(s) 104 may include or be operably coupled to one or more displays for providing bowel movement event data and/or subject health data to a user, such as a healthcare provider or a subject using wearable device 102.

The system 100, and more particularly the wearable device 102, may be modified in various ways. For example, the transmitter 120 may be coupled to the remote device(s) 104 via wired communication, or the processor 112 may be operatively coupled to one or more of the other components of the electronic assembly 108 via wireless communication. Similarly, in some embodiments, the wearable device 102 may lack the processor 112, and the sensor(s) 110, 114, 115 may instead be operatively coupled to a processor of the remote device 104, such as a processor of a smartphone. In some embodiments, wearable device 102 may not include user interface 122 and instead rely on wireless communication with remote device(s) 104 to communicate information to and/or receive instructions from a user. As a further example, in some embodiments, there may be two or more wearable devices 102, each wearable device 102 being operatively coupled to a remote device 104. For example, there may be a first wearable device 102 in the form of a smart watch and a second wearable device 102 in the form of a patch configured to be carried on the torso of a subject, where the two wearable devices are operatively coupled to a remote device 104 (e.g., a smart phone). In such embodiments, all of the wearable devices may include similar sensors 110, 114, and/or 115, or the wearable devices may include different sensors 110, 114, and/or 115. As a particular example, a first wearable device 102 in the form of a smart watch may include an audio sensor and an accelerometer/motion sensor that function as a bowel movement event sensor, while a second wearable device 102 in the form of a torso patch may include other types of bowel movement event sensors (e.g., a gas sensor, a mechanical electromyography sensor, and/or an electromyography sensor). Because all of the wearable devices are operatively coupled to the remote device(s) 104, the remote device(s) 104 can detect a bowel movement event using input from all of the operatively coupled wearable devices.

Referring now to fig. 2 and 3, a wearable device 202 is illustrated in accordance with an embodiment of the present disclosure. The wearable device 202 is a more specific embodiment of the wearable device 102 described above. Thus, the wearable device 202 includes some of the same components and operates in a similar manner as the wearable device 102 described above. As described above, the wearable device 202 is also operatively coupled to one or more remote devices 104. The wearable device 202 includes a base or housing 206 configured to be removably worn by a subject. More specifically, the substrate 206 is a patch that may be adhesively secured to the torso of a subject. The patient facing side 222 of the base 206 may be adhesively secured to the subject. The patient facing side 222 of the base 206 also includes one or more bowel movement event sensors 214, more specifically, includes a plurality of electromyography electrodes 224 for sensing abdominal muscle electrical signals of the subject. The base 206 also carries one or more wake-up sensors 210, and more particularly, also carries an elongated optical sensor 226, the elongated optical sensor 226 configured to be disposed under the lower body garment and to sense when the subject removes the lower body garment. The base 206 internally carries the processor 212 and a power supply 218. In some embodiments, the base 206 may carry additional sensors, such as any of an inertial measurement unit, a gas sensor, a mechanical myograph sensor, or other sensors contemplated herein.

Referring to fig. 4A and 4B, a wearable device 302 according to another embodiment of the present disclosure is illustrated. The wearable device 302 is another more specific embodiment of the wearable device 102 described above. Thus, the wearable device 302 includes some of the same components and operates in a similar manner as the wearable device 102 described above. As described above, the wearable device 302 is also operatively coupled to one or more remote devices 104. The wearable device 302 includes a base 306 configured to be removably worn by a subject. More specifically, the substrate 306 is a patch that may be adhesively secured to the torso of a subject. The patient-facing side 322 of the base 306 may be adhesively secured to the subject. The patient facing side 322 of the base 306 further includes one or more bowel movement event sensors 314, more specifically, a plurality of electromyography electrodes 324 for sensing abdominal muscle electrical signals of the subject and a mechanical electromyography sensor 325 (fig. 4B) for sensing abdominal muscle movement signals of the subject. The opposite side 328 of the base 306 removably carries a hub 330 (fig. 4A), the hub 330 being operatively coupled to the bowel movement event sensor(s) 314. Hub 330 carries a processor (not shown), a memory (not shown) and a power source (not shown). Hub 330 may be removable from base 306 to facilitate recharging the power supply. Hub 330 also carries a user interface 320 (fig. 4A), more specifically, one or more lights 332 and a speaker 334, for providing information to a subject. In some embodiments, the base 306 may carry additional sensors, such as any of optical sensors, audio sensors, inertial measurement units, gas sensors, or other sensors contemplated herein.

Referring to fig. 5, a wearable device 402 according to yet another embodiment of the present disclosure is illustrated. Wearable device 402 is yet another more specific embodiment of wearable device 102 described above. Thus, wearable device 402 includes some of the same components and operates in a similar manner as wearable device 102 described above. As described above, the wearable device 402 is also operatively coupled to one or more remote devices 104. The wearable device 402 includes a base 406 configured to be removably worn by a subject. More specifically, the base 406 is a waistband that is extendable around and securable to the torso of the subject. The patient facing side 422 of the base 406 also includes one or more bowel movement event sensors 414, more specifically, a plurality of electromyography electrodes 424, an inertial measurement unit 436, and a mechanical electromyography sensor 438. In some embodiments, the base 406 may carry additional sensors, such as any of the optical sensors, audio sensors, or other sensors contemplated herein.

Referring now to fig. 6-8, a wearable device 502 is illustrated in accordance with yet another embodiment of the present disclosure. Wearable device 502 is yet another more specific embodiment of wearable device 102 described above. Thus, wearable device 502 includes some of the same components and operates in a similar manner as wearable device 102 described above. As described above, the wearable device 502 is also operatively coupled to one or more remote devices 104. The wearable device 502 includes a base or housing 506 configured to be removably worn by a subject. More specifically, the base 506 carries a patch 540 (fig. 6) that may be adhesively secured to the torso of the subject. The base 506 also carries one or more bowel movement event sensors 514 (fig. 8). More specifically, the base 506 carries a mechanical myogram sensor 524 for sensing a subject's abdominal muscle movement signal and a temperature sensor 542 for sensing a change in intestinal temperature. The base 506 internally carries a processor 512 (fig. 8) and a battery (not shown). The base 506 further carries a user interface 520 (fig. 7). The user interface 520 includes a first input 544, the first input 544 being actuatable to reconfigure the device 502 from the sleep mode to the active mode. The user interface 520 further includes a second input 546, the second input 546 being actuatable to reconfigure the device 502 from the active mode to the sleep mode. In some embodiments, the base 506 may carry additional sensors, such as any of an optical sensor, an inertial measurement unit, an audio sensor, a gas sensor, an electromyography sensor, or other sensors contemplated herein.

The system according to embodiments of the present disclosure may determine the occurrence of a bowel movement event in various ways. For example, in some embodiments, if at least a certain number of the bowel movement event sensors 114 sense a corresponding stimulus (as a more specific example, if most of the bowel movement event sensors 114 sense a corresponding stimulus), the system may determine the occurrence of a bowel movement event. In other embodiments, the system may determine the occurrence of a bowel movement event if all of the bowel movement event sensors 114 sense corresponding stimuli. In some embodiments, the system may use machine learning to improve the accuracy of determining the occurrence of a bowel movement event for a particular subject. More specifically, some systems may identify that one or more particular stimuli are continually sensed before or during a bowel movement event for a particular subject, and when determining the occurrence of a bowel movement event, the system may weight the information received from the corresponding sensor more heavily. In some embodiments, if the bowel movement event sensor 114 senses a corresponding stimulus according to a particular sequence or algorithm, the system may determine that a bowel movement event has occurred. Similarly, in some embodiments, if wake-up sensor 110 senses a corresponding stimulus according to a particular sequence or algorithm, the system may reconfigure the device from a sleep mode to an active mode.

Referring to fig. 9, a method 600 for sensing a bowel movement event of a subject in accordance with an embodiment of the present disclosure is illustrated. Before beginning method 600, a wearable device, such as any of the wearable devices contemplated herein, is attached to the torso of the subject. The method 600 begins at block 602 by: the increased light is sensed by an optical sensor of the wearable device when the subject removes the lower body clothing. The method 600 continues at block 604 by: the abdominal muscle movement signal of the subject is sensed by a mechanical myogram sensor of the wearable device. The method 600 ends at block 606 by: the occurrence of a bowel movement event is determined based at least in part on the sensed abdominal muscle movement signal of the subject and the increased light sensed when the subject removes the lower body clothing.

The method 600 may be modified in various ways and/or include various additional actions. For example, the method 600 may further include reconfiguring the wearable device from a sleep mode, in which the mechanical myogram sensor is inactive, to an active mode, in which the mechanical myogram sensor is configured to sense an abdominal muscle movement signal of the subject based on the increased light sensed when the subject removes the lower body clothing. As another example, method 600 may further include sensing flatus by a gas sensor of the wearable device, and the determination that the bowel movement event has occurred may be based at least in part on the sensed flatus. As yet another example, the method 600 may further include sensing, by the inertial measurement unit of the wearable device, a standing motion of the subject after sensing the flatus, and the determination that the bowel movement event has occurred may be based at least in part on the sensed standing motion. As yet another example, the method 600 may further include sensing, by the inertial measurement unit of the wearable device, a sitting motion of the subject prior to sensing the abdominal muscle movement signal of the subject, and the determination that the bowel movement event has occurred may be based at least in part on the sensed sitting motion. As another example, the method 600 may be repeated to determine a plurality of bowel movement events over a particular period of time, such as days, weeks, months, or years. As an alternative example, the sensing of the abdominal muscle movement signal in block 604 may precede the sensing of the increased light in block 602.

Fig. 10-16 illustrate actions associated with another method for sensing a bowel movement event of a subject in accordance with embodiments of the present disclosure. Before starting the method, a wearable device 702, such as any of the wearable devices contemplated herein, is attached to the torso of subject S. As shown in fig. 10, the method starts by: (A) The abdominal muscle movement signal of the subject S is sensed by a mechanical myogram sensor (not shown) of the wearable device 702; (B) The temperature change of the intestine of the subject is sensed by a temperature sensor (not shown) of the wearable device 702; and/or (C) sense sound emanating from the intestinal tract of the subject by an audio sensor (not shown) of the wearable device 702. As shown in fig. 11, the method continues by: the increased light is sensed by an optical sensor (not shown) of the wearable device 702 when the subject S removes the lower body clothing. As shown in fig. 12, the method next includes sensing, by an inertial measurement unit (not shown) of the wearable device 702, a sitting motion of the subject S. As shown in fig. 13, the method continues by: the abdominal muscle movement signal of the subject S is sensed by a mechanical myogram sensor (not shown) of the wearable device 702. As shown in fig. 14, the method next includes sensing flatus by a gas sensor (not shown) of wearable device 702. As shown in fig. 15, the method continues by: the standing motion of subject S is sensed by an inertial measurement unit (not shown) of wearable device 702. As shown in fig. 16, the method next includes (a) sensing, by an inertial measurement unit (not shown) of a remote device, illustratively a smart watch 704 worn by subject S, hand motion for flushing a toilet; and/or (B) the toilet flush sound is sensed by an audio sensor (not shown) of the remote device. The method ends by: the occurrence of a bowel movement event is determined based at least in part on one or more of the previously sensed stimuli. The methods presented in fig. 10-16 for sensing a bowel movement event may be modified by omitting one or more of the previously presented steps, adding additional steps between some of the previously presented steps, and/or by rearranging the order of the presented steps. For example, in some embodiments, the method may not include sensing a change in intestinal temperature and/or sound emanating from the intestinal tract of the subject (as shown in fig. 10), but may instead begin with sensing increased light when subject S removes lower body clothing (as shown in fig. 11). In some embodiments, the method may omit sensing of flatus (as shown in fig. 14). In yet further embodiments, the method may omit sensing of the attitude change by the inertial measurement unit, as shown in fig. 13 and 15. Any sequence of all or any subset of the presented steps may be used to determine the occurrence of a bowel movement event.

The sensitivity and specificity of the systems and methods described herein for detecting a bowel movement event in a subject may be improved using machine learning techniques. Referring to fig. 17, a method 800 for training one or more processors to detect a bowel movement event is illustrated in accordance with an embodiment of the present disclosure. Prior to starting method 800, a first wearable device (such as a smart watch or any of the wearable devices contemplated herein) and a second wearable device (such as any of the wearable devices contemplated herein) may be provided to a subject for attachment to the subject's body. Both of these wearable devices may take the form of the wearable device 102 described herein (see, e.g., fig. 1). The first wearable device may include a first bowel movement event sensor 114 configured to sense one or more first stimuli, and the second wearable device may include a second bowel movement event sensor 114 configured to sense one or more second stimuli. The first wearable device and the second wearable device are operatively coupled to the one or more processors wirelessly or through a wired connection in any of the manners contemplated herein. In some embodiments, the one or more processors may include a first processor operatively coupled to (e.g., embedded within or in wireless communication with) a first wearable device and a second processor operatively coupled to (e.g., embedded within or in wireless communication with) a second wearable device. In such an embodiment, the first processor may be operably coupled to the second processor such that the two processors may work together to implement method 800. In some embodiments, the one or more processors may be comprised of a single processor operatively coupled to both the first wearable device and the second wearable device. The single processor may reside on a first wearable device, a second wearable device, or a remote device 104 (e.g., a smart phone) operatively coupled to both wearable devices.

The method 800 begins at block 802 by: the one or more first bowel movement stimuli are sensed by one or more first bowel movement event sensors carried by the first wearable device. The method 800 continues at block 804 by: one or more second stimuli associated with one or more bowel movement events of the subject are sensed by one or more second bowel movement event sensors carried by a second wearable device. The stimulus sensed (by both the first and second bowel movement event sensors) may be any of the stimuli contemplated herein. In some embodiments, the first and second bowel movement stimuli may be different types of stimuli. For example, the second bowel movement event sensor may be an EMG electrode and/or an MMG sensor, and the second stimulus may be an abdominal muscle electrical signal and/or an abdominal muscle movement signal of the subject, and the first bowel movement event sensor may be an inertial measurement unit, and the first stimulus may be movement of the subject. In other embodiments, the first and second bowel movement stimuli may be the same type of stimulus. The method 800 continues at block 806 by: determining, by the one or more processors, occurrence of a bowel movement event of the detected subject based on the second stimulus. This determination may be accomplished by any of the methods discussed herein. The method 800 continues at block 808 by: the first stimulus sensed by the first bowel movement event sensor is associated with a bowel movement event of the subject by the one or more processors within a predetermined period of time of the detected bowel movement event (e.g., within 60, 120, 180, and/or 240 seconds before and/or after the detected bowel movement event). The one or more processors may then train a machine learning algorithm for detecting a bowel movement event of the subject using data indicative of a first stimulus sensed by the first bowel movement event sensor that has been associated with the bowel movement event of the subject.

The method 800 may be useful for training a machine learning algorithm implemented on one or more processors to detect a bowel movement event of a subject using a first stimulus sensed by a first bowel movement event sensor. In particular, the method 800 may be useful in the following cases: wherein the second defecation event sensor is initially capable of detecting a defecation event with a higher sensitivity and/or specificity than the first defecation event sensor, but it is desirable to ultimately use only or primarily the first defecation event sensor to detect a defecation event.

As an illustrative and non-limiting example, the second wearable device may be a patch configured to be secured to the torso of a subject, as described herein. The patch may include an MMG sensor, an EMG sensor, an ECG sensor, an optical sensor, a gas sensor, and/or other sensors as described herein. The first wearable device may be a smart watch configured to be worn on a wrist of a subject. The smart watch may include additional or different sensors, such as an accelerometer/Inertial Measurement Unit (IMU) and/or an audio sensor. The second wearable device is initially able to detect a bowel movement event with a higher specificity and sensitivity than the first wearable device, but may be more invasive and/or inconvenient for the subject to wear for an extended period of time. Thus, it may be desirable to ultimately train the one or more processors to detect a bowel movement event using only the first wearable device (i.e., the smart watch) without the assistance of the second wearable device. To accomplish this training, when the second wearable device detects a bowel movement event using the techniques described herein, it may instruct the one or more processors to associate movement and/or audio signals recorded by the smart watch shortly before (e.g., within 60 or 120 seconds before or after the detected bowel movement event) with the bowel movement. In this way, the second wearable device provides a reference truth tag that allows the smart watch to discern movement and/or audio signals associated with bowel movements and/or audio signals not associated with bowel movements. Over time, as the wearable patch trains the processor(s), the processor(s) may use the stimulus sensed by the smart watch to predict and/or detect bowel movement with greater accuracy. Eventually, when the smart watch (or mobile device) is sufficiently trained, the subject may stop wearing the wearable patch and rely solely on the smart watch to detect the bowel movement event.

Training of the machine learning algorithm at the one or more processors may be accomplished using any known machine learning technique. For example, the one or more processors may employ a neural network having a plurality of layers of nodes to predict or detect a bowel movement event based on the stimulus sensed by the first bowel movement event sensor. The sensitivity and specificity of such neural networks may be improved by adjusting weights associated with such node layers using baseline truth data that provides examples of first stimuli associated with bowel movement events and examples of first stimuli not associated with bowel movement events. As described herein, such baseline truth data may be provided by a second bowel movement event sensor on a second wearable device. Weights within the neural network may be adjusted using an iterative training process that compares predicted outputs based on particular first stimuli with a baseline truth tag provided by a second wearable device that indicates whether such first stimuli are associated with a bowel movement event. If the prediction of the neural network does not match the reference truth label, the weights may be adjusted according to a penalty function to improve the match between the prediction of the network and the reference truth label. In this way, by providing a baseline truth tab indicating whether a particular first stimulus is associated with a bowel movement event, the weights of the neural network can be adjusted to improve the detection of a bowel movement event by the network based solely on the first stimulus.

The method 800 may modify and/or include various additional actions in various ways. For example, if one or more bowel movement event sensors of the first wearable device sense corresponding stimuli according to a particular sequence or algorithm, the one or more processors may be trained to determine the occurrence of a bowel movement event. As another example, the first wearable device may further include a wake sensor configured to sense one or more wake stimuli. The first wearable device may be further configured to transition from a sleep mode to an active mode when the wake sensor senses a wake stimulus associated with a bowel movement event of the subject, in which the first wearable device is configured to sense the bowel movement event of the subject via the first bowel movement event sensor. The method 800 may be modified by having the one or more processors associate wake stimuli sensed by the wake sensor with the bowel movement event within a second predetermined period of time of the detected bowel movement event (e.g., within 60, 120, 180, and/or 240 seconds before and/or after the detected bowel movement event). The second predetermined period of time may be the same as the predetermined period of time or may be different from the predetermined period of time. In this way, the one or more processors may be trained to detect not only bowel movement events with greater sensitivity and specificity, but also wake up from sleep mode to active mode, thereby detecting such bowel movement events with greater accuracy. This may help to save power used by the one or more processors and/or the first wearable device.

Referring to fig. 18, another method 900 for training one or more processors to detect a bowel movement event is illustrated in accordance with an embodiment of the present disclosure. Before beginning method 900, a wearable device (such as any of the wearable devices contemplated herein) may be provided to a subject for attachment to the body of the subject. The wearable device may take the form of the wearable device 102 described herein (see, e.g., fig. 1), and may include a bowel movement event sensor 114 configured to sense one or more stimuli. The wearable device and the bowel movement event sensor provided therein may be operatively coupled to one or more processors. The one or more processors may be provided on or in wireless communication with the wearable device. For example, the one or more processors may reside on a remote device 104 (e.g., a smart phone).

The method 900 begins at block 902 by: one or more stimuli are sensed by a bowel movement event sensor of the wearable device. The method 900 continues at block 904 by: user input is received via a mobile device of the subject (e.g., remote device 104) indicating a point in time of bowel movement at which a bowel movement event occurred. The user input may include manual input from the subject (e.g., actuation of physical and/or virtual buttons on a touch screen, voice input) that occurs at the time the user provided input for the bowel movement event. Alternatively or additionally, the user input may include manual input from the subject indicating that a bowel movement event occurred at a particular point in time in the past. Alternatively or additionally, the user input may include manual input from the subject indicating that a bowel movement event is imminent (e.g., the subject is about to perform a bowel movement).

The method 900 continues at block 906 by: the stimulus sensed by the bowel movement event sensor is associated with a bowel movement event by the one or more processors within a predetermined period of time of a bowel movement time point received from the subject (e.g., within 60 seconds, 120 seconds, 180 seconds, and/or 240 seconds before and/or after the bowel movement time point). The one or more processors may then train a machine learning algorithm for detecting a bowel movement event of the subject using data indicative of a first stimulus sensed by the first bowel movement event sensor that has been associated with the bowel movement event of the subject. In this way, a machine learning algorithm implemented by the one or more processors for detecting a bowel movement event based on the stimulus sensed by the bowel movement event sensor may be trained using baseline truth data manually provided by the subject. Such training may be accomplished using any of the techniques described herein.

While this invention has been shown and described as having a preferred design, the present invention can be modified within the spirit and scope of this disclosure. This application is therefore intended to cover any variations, uses, or adaptations of the invention using its general principles. Further, this application is intended to cover such departures from the present disclosure as come within known or customary practice in the art to which this invention pertains.

Claims

1. A system for sensing a bowel movement event of a subject, the system comprising:

a wearable device configured to be carried on a torso of a subject, the wearable device operable in a sleep mode and an active mode, the wearable device comprising:

a wake-up sensor configured to sense a first stimulus, and

a mechanical myograph sensor configured to sense an abdominal muscle movement signal of the subject; and

a processor operatively coupled to the wake sensor and the mechanical myogram sensor, the processor configured to switch the wearable device from a sleep mode to an active mode based on a first stimulus sensed by the wake sensor, and in the active mode, the wearable device is configured to communicate with the processor to determine an occurrence of a bowel movement event of the subject based on an abdominal muscle movement signal sensed by the mechanical myogram sensor.

2. The system of claim 1, wherein the subject's abdominal muscle movement signal is a second stimulus, the system further comprising a third sensor operatively coupled to the processor and configured to sense the third stimulus, and in the active mode, the processor is configured to determine the occurrence of a bowel movement event in the subject based on the abdominal muscle movement signal sensed by the mechanical myograph sensor and the third stimulus sensed by the third sensor.

3. The system of claim 2, wherein the third sensor comprises a gas sensor disposed in a wearable device and configured to sense flatus.

4. The system of claim 2, wherein the third sensor is an audio sensor configured to sense toilet flushing sound.

5. The system of claim 2, wherein the third sensor is an electromyography electrode configured to sense an electrical muscle signal of the subject.

6. The system of claim 2, wherein the third sensor is an inertial measurement unit configured to sense a change in posture of a subject.

7. The system of any of claims 1-6, wherein the wearable device further comprises a patch configured to be carried on a torso of a subject, the patch carrying a wake-up sensor and a mechanical actuation sensor.

8. The system of claim 7, wherein the patch further carries a processor.

9. The system of any of claims 1-6, wherein the wearable device further comprises a waistband configured to extend around a torso of a subject, the waistband carrying a wake-up sensor and a mechanical myogram sensor.

10. The system of claim 9, wherein the waistband further carries a processor.

11. The system of any of claims 1-10, wherein the wake sensor comprises one of an optical sensor and a resistance sensor configured to sense when a subject removes lower body clothing.

12. The system of any of claims 1-11, wherein the wearable device further comprises a health sensor configured to sense a health stimulus associated with the health of the subject.

13. The system of claim 12, wherein the health sensor comprises a blood sensor configured to sense blood in the stool of a subject.

14. The system of claim 13, wherein the blood sensor comprises a solid state vapor detection sensor configured to sense one or more volatile organic compounds.

15. A system for sensing a bowel movement event of a subject, the system comprising:

a wearable device configured to be carried on a torso of a subject, the wearable device comprising:

a mechanical myograph sensor configured to sense an abdominal muscle movement signal of the subject;

a gas sensor configured to sense flatus; and

A processor operatively coupled to the mechanical myograph sensor and the gas sensor, the processor configured to determine an occurrence of a bowel movement event of the subject based on the abdominal muscle movement signal sensed by the mechanical myograph sensor and the flatus sensed by the gas sensor.

16. The system of claim 15, wherein the processor is configured to determine an occurrence of a bowel movement event of the subject based on a sequence of events including one of an abdominal muscle movement signal sensed by the mechanical myogram sensor and a flatus sensed by the gas sensor preceding the other of the abdominal muscle movement signal sensed by the mechanical myogram sensor and the flatus sensed by the gas sensor.

17. The system of any of claims 15-16, wherein the wearable device further comprises a base carrying a mechanical actuation map sensor, a gas sensor, and a processor.

18. A system for sensing a bowel movement event of a subject, the system comprising:

a wearable device configured to be carried on a torso of a subject and under lower body clothing worn by the subject, the wearable device comprising

An optical sensor configured to sense increased light when the subject removes lower body clothing; and

a processor is operably coupled to the optical sensor, the processor configured to determine an occurrence of a bowel movement event based at least in part on the increased light sensed by the optical sensor.

19. The system of claim 18, wherein the wearable device is operable in an active mode and a sleep mode, and the processor is configured to switch the wearable device from the sleep mode to the active mode upon determining that the subject removes lower body clothing in response to the increased light sensed by the optical sensor.

20. The system of claim 19, wherein the optical sensor is a first sensor configured to sense light as a first stimulus, wherein the wearable device further comprises a second sensor configured to sense a second stimulus when the wearable device is in an active mode, the second stimulus being different from the first stimulus, and wherein the processor is operably coupled to the second sensor, the processor configured to determine an occurrence of a bowel movement event of the subject based on a signal received from the second sensor.

21. The system of claim 19, wherein the optical sensor is a first sensor configured to sense light as a first stimulus, wherein the wearable device further comprises a second sensor configured to sense a second stimulus when the wearable device is in an active mode, the second stimulus being different from the first stimulus, and wherein the processor is operably coupled to the second sensor, the processor configured to determine an occurrence of a bowel movement event for the subject based on signals received from the first sensor and the second sensor.

22. The system of any one of claims 20-21, wherein the second sensor is a mechanical myograph sensor.

23. The system of any of claims 18-22, wherein the wearable device further comprises a patch configured to be carried on a torso of a subject, the patch carrying an optical sensor and a processor.

24. The system of any of claims 18-22, wherein the wearable device further comprises a waistband configured to extend around a torso of the subject, the waistband carrying the optical sensor and the processor.

25. A method for sensing a bowel movement event in a subject, the method comprising:

sensing, by an optical sensor of a wearable device carried on a torso of a subject, increased light when the subject removes lower body clothing;

sensing, by a mechanical myogram sensor of the wearable device, an abdominal muscle movement signal of the subject; and

the occurrence of the bowel movement event is determined based at least in part on the increased light sensed when the subject removes the lower body clothing and the sensed abdominal muscle movement signal of the subject.

26. The method of claim 25, further comprising sensing flatus by a gas sensor of the wearable device, and wherein the determination that a bowel movement event has occurred is based at least in part on the sensed flatus.

27. The method of any of claims 25-26, further comprising sensing, by an inertial measurement unit of the wearable device, a sitting motion of the subject prior to sensing an abdominal muscle movement signal of the subject, and wherein the determination that a bowel movement event has occurred is based at least in part on the sensed sitting motion.

28. The method of any of claims 25-27, further comprising, after sensing flatus, sensing a standing motion of the subject by an inertial measurement unit of the wearable device, and wherein the determination that a bowel movement event has occurred is based at least in part on the sensed standing motion.

29. The method of any one of claims 25-28, further comprising sensing a plurality of bowel movements events of the subject over a period of time.

30. The method of claim 29, wherein sensing each of a plurality of bowel movements events of the subject comprises:

sensing, by a mechanical myogram sensor of the wearable device, an abdominal muscle movement signal of the subject;

sensing, by the optical sensor, increased light when the subject removes lower body clothing; and

the occurrence of each of the plurality of bowel movement events is determined based at least in part on the sensed abdominal muscle movement signal of the subject and the increased light sensed when the subject removes the lower body clothing.

31. The method of any one of claims 25-30, wherein sensing, by the optical sensor, the increased light precedes sensing, by the mechanical myogram sensor, an abdominal muscle movement signal of the subject when the subject removes lower body clothing.

32. The method of any one of claims 25-30, wherein sensing, by the mechanical myogram sensor, the subject's abdominal muscle movement signal precedes sensing, by the optical sensor, increased light when the subject removes lower body clothing.

33. The method of any of claims 25-30, further comprising reconfiguring the wearable device from a sleep mode to an active mode based on increased light sensed when the subject removes lower body clothing, in the sleep mode the mechanical electromyography sensor is inactive, and in the active mode the mechanical electromyography sensor is configured to sense abdominal muscle movement signals of the subject.

34. A system for training one or more processors to detect a bowel movement event of a subject, the system comprising:

a first wearable device, wherein the first wearable device is configured to be carried on a body of a subject, the first wearable device comprising a first bowel movement event sensor configured to sense one or more first stimuli;

a second wearable device configured to be carried on a body of a subject, the second wearable device comprising a second bowel movement event sensor configured to sense one or more second stimuli; and

one or more processors operatively coupled to the first and second bowel movement event sensors and configured to determine an occurrence of a bowel movement event of the detected subject based on the one or more second stimuli and to associate the first stimulus sensed by the first bowel movement event sensor with the bowel movement event of the subject within a predetermined period of the detected bowel movement event.

35. The system of claim 34, wherein the one or more processors are further configured to train a machine learning algorithm for detecting a bowel movement event of the subject using data indicative of a first stimulus sensed by the first bowel movement event sensor that has been associated with the bowel movement event of the subject.

36. The system of any one of claims 34-35, wherein:

the first wearable device further comprises a wake-up sensor configured to sense one or more wake-up stimuli,

the first wearable device is configured to transition from a sleep mode to an active mode when the wake sensor senses a wake stimulus associated with a bowel movement event of the subject, in which the first wearable device is configured to sense a bowel movement event of the subject via the first bowel movement event sensor; and

the one or more processors are further configured to associate a wake stimulus sensed by the wake sensor during a second predetermined time period of the detected bowel movement event with the bowel movement event of the subject.

37. The system of any of claims 34-36, wherein the one or more processors comprise a first processor operably coupled to a first bowel movement event sensor and a second processor operably coupled to a second bowel movement event sensor, wherein the first and second processors are operably coupled to each other.

38. The system of any of claims 34-36, wherein the one or more processors consist of a single processor operably coupled to a first bowel movement event sensor and a second bowel movement event sensor.

39. The system of any of claims 34-38, wherein the first wearable device comprises a smart watch.

40. The system of any of claims 34-39, wherein the second wearable device comprises a patch configured to be carried on a torso of a subject.

41. The system of any of claims 34-40, wherein the first wearable device comprises at least one of the one or more processors.

42. The system of any of claims 34-40, wherein at least one of the one or more processors is in wireless communication with a first wearable device.

43. The system of any one of claims 34 to 42, wherein the first and second stimuli are different types of stimuli.

44. The system of any one of claims 34 to 42, wherein the first and second stimuli are the same type of stimulus.

45. A method for training one or more processors operatively coupled to a first wearable device to detect a bowel movement event of a subject, the method comprising:

sensing, by a first bowel movement event sensor carried by a first wearable device, one or more first stimuli;

Sensing, by a second bowel movement event sensor carried by a second wearable device, one or more second stimuli;

determining, by the one or more processors, occurrence of a bowel movement event of the detected subject based on the second stimulus; and

the one or more processors correlate, with the detected bowel movement event, a first stimulus sensed by the first bowel movement event sensor over a predetermined period of time of the bowel movement event with the bowel movement event of the subject.

46. The method of claim 45, further comprising training, by the one or more processors, a machine learning algorithm for detecting a bowel movement event of the subject using data indicative of a first stimulus sensed by the first bowel movement event sensor that has been associated with the bowel movement event of the subject.

47. The method of claim 45, wherein the first wearable device further comprises a wake sensor configured to sense one or more wake stimuli, the first wearable device configured to transition from a sleep mode to an active mode when the wake sensor senses a wake stimulus associated with a bowel movement event of the subject, in the active mode the first wearable device configured to sense a bowel movement event of the subject via the first bowel movement event sensor, the method further comprising:

The wake stimulus sensed by the wake sensor during a second predetermined time period of the detected bowel movement event is associated with the bowel movement event of the subject by the one or more processors.

48. The method of any of claims 45-47, wherein the one or more processors comprise a first processor operably coupled to a first bowel movement event sensor and a second processor operably coupled to a second bowel movement event sensor, wherein the first processor and the second processor are operably coupled to each other.

49. The method of any of claims 45-48, wherein the one or more processors consist of a single processor operably coupled to a first bowel movement event sensor and a second bowel movement event sensor.

50. The method of any of claims 45-48, wherein the first wearable device comprises a smart watch.

51. The method of any of claims 45-50, wherein the second wearable device comprises a patch configured to be carried on a torso of a subject.

52. The method of any of claims 45-51, wherein the first wearable device comprises at least one of the one or more processors.

53. The method of any of claims 45-51, wherein at least one of the one or more processors is in wireless communication with a first wearable device.

54. The method of any one of claims 45-52, wherein the first stimulus and second stimulus are different types of stimulus.

55. The method of any one of claims 45-52, wherein the first stimulus and the second stimulus are the same type of stimulus.

56. A system for training one or more processors to detect a bowel movement event of a subject, the system comprising:

a wearable device, wherein the wearable device is configured to be carried on a body of a subject, the wearable device comprising a bowel movement event sensor configured to sense one or more stimuli;

a mobile device configured to receive user input from a subject indicating a bowel movement time point at which a bowel movement event occurred; and

one or more processors operatively coupled with the bowel movement event sensor and the mobile device configured to correlate the stimulus sensed by the bowel movement event sensor with a bowel movement event of the subject within a predetermined period of time at a point in time of the bowel movement.

57. The system of claim 56, wherein the one or more processors are further configured to train a machine learning algorithm for detecting a bowel movement event of the subject using data indicative of a first stimulus sensed by the first bowel movement event sensor that has been associated with the bowel movement event of the subject.

58. The system of claims 56-57, wherein:

the wearable device further includes a wake sensor configured to sense one or more wake stimuli;

the wearable device is configured to transition from a sleep mode to an active mode when the wake sensor senses a wake stimulus associated with a bowel movement event of the subject, in which active mode the wearable device is configured to sense a bowel movement event of the subject via the bowel movement event sensor; and

59. The system of any of claims 56-58, wherein the mobile device comprises at least one of the one or more processors.

60. The system of any of claims 56-59, wherein the wearable device comprises at least one of the one or more processors.

61. The system of any of claims 56-60, wherein the wearable device comprises at least one of a smart watch and a patch configured to be carried on a torso of a subject.

62. A method for training one or more processors operatively coupled to a wearable device to detect a bowel movement event of a subject, the method comprising:

sensing one or more stimuli by a bowel movement event sensor carried by the wearable device;

receiving, via a mobile device of a subject, user input indicating a bowel movement time point at which a bowel movement event occurred; and

the one or more processors correlate the stimulus sensed by the bowel movement event sensor with a bowel movement event of the subject within a predetermined period of time at a point in time of the bowel movement.

63. The method of claim 62, further comprising training, by the one or more processors, a machine learning algorithm for detecting a bowel movement event of the subject using data indicative of a first stimulus sensed by the first bowel movement event sensor that has been associated with the bowel movement event of the subject.

64. The method of any of claims 62-63, wherein the wearable device further comprises a wake sensor configured to sense one or more wake stimuli, the wearable device configured to transition from a sleep mode to an active mode when the wake sensor senses a wake stimulus associated with a bowel movement event of a subject, the method further comprising:

65. The method of any of claims 62-64, wherein the mobile device comprises at least one of the one or more processors.

66. The method of any of claims 62-65, wherein the wearable device comprises at least one of the one or more processors.

67. The method of any of claims 62-66, wherein the wearable device comprises at least one of a smart watch and a patch configured to be carried on a torso of a subject.