WO2019149331A1

WO2019149331A1 - A system for monitoring physical movements of a user

Info

Publication number: WO2019149331A1
Application number: PCT/DK2019/050044
Authority: WO
Inventors: Søren WÜRTZ; Finn Bech ANDERSEN; Jønne MARCHER; Carsten SCHEIBYE
Original assignee: Precure Aps
Priority date: 2018-02-05
Filing date: 2019-02-05
Publication date: 2019-08-08
Also published as: EP3750167A1

Abstract

The present invention relates to a system for monitoring physical movements of a user, the system comprising: a detection unit configured for receiving a signal obtained by a sensor device representative of a sensed muscular activity, detecting or evaluating, if present, in said received signal, one or more inexpedient muscular activities (IMA). The invention also relates to a method for monitoring a physical movement of a user.

Description

A SYSTEM FOR MONITORING PHYSICAL MOVEMENTS OF A USER

FIELD OF THE INVENTION

The present invention relates to a system for monitoring and preferably evaluating physical movements of a user, the system comprising : a detection unit configured for receiving a signal obtained by a sensor device representative of a sensed muscular activity, detecting or preferably evaluating, if present, in said received signal, one or more inexpedient muscular activities (IMA), and if detected communicate, e.g. to the user wearing the said sensor device, that an inexpedient muscular activity has been detected. The invention also relates to a method for monitoring and evaluating physical movement of a user.

BACKGROUND OF THE INVENTION

At present, most prevention and rehabilitation efforts associated with muscular damage, the tendons and their origins and insertions - known as Musculoskeletal Disorder (MSD) - are addressed manually by instructing a person in the correct use of his/her muscular function. This procedure is expensive, time consuming and has limited lasting effect. The few technical solutions, which exist, are characterised by being equipment-intensive and requires a professional handling the setup and use. This accounts for instance patent US2012071732, where the use is restricted by data being transferred by wire. The same patent is also limited in its use as it only enables measurement and not evaluation. Likewise, SEMG is today primarily used to measure, leaving diagnostics and movement analyses to professionals. This accounts for instance for the patents US2016100775 and US2015070270.

Hence, an improved method to monitor muscular activity would be advantageous This may be used in an attempt e.g. to reduce the impact and frequency of MSD would be advantageous, and in particular a more efficient and/or reliable way to create lasting changes in the users ^' behaviour would be advantageous.

OBJECT OF THE INVENTION It is a further object of the present invention to provide an alternative to the prior art.

In particular, it may be seen as an preferred object of the present invention to provide a method to reducing the impact and frequency of MSD that solves the above mentioned problems of the prior art by creating lasting changes in users' behaviour through biofeedback.

SUMMARY OF THE INVENTION

Thus, the above described object and several other objects are intended to be obtained in a first aspect of the invention by providing a system for monitoring and , preferably evaluating physical movements of a user, the system comprising : a detection unit configured for

- receiving a signal obtained by a sensor device representative of a sensed muscular activity,

detecting or evaluating, if present, in said received signal, one or more inexpedient muscular activities (IMA), and if detected preferably communicate, e.g. to the user wearing the said sensor device, that an inexpedient muscular activity has been detected.

It is noted that within the meaning of physical movement is considered a static use of one or more muscles not necessarily giving rise to a physical movement of a body part as well as use of muscles resulting a physical movement of a body part.

"EMG" and "SEMG" are preferably used interchangeably herein. Preferred embodiments of the invention also have scientific relevance because it may generate new knowledge about muscle activity and strain patterns in healthy people and e.g. people with epikondylitis lateralis (EL) or others. The present invention may in some embodiments allow to map this during everyday activities for very long periods (whole days). In some embodiments, it may also be possible to measure the strain exposure to which people are exposed to in their daily lives. It is noted that the invention does not provide any diagnosis in medical terms since it "only" detects IMAs, and may give the user an appropriate warning of the IMA and simple guidance on correction of muscular activity to avoid IMA. In order to reach a diagnosis, a medical practitioner will have to include other means such as e.g. x-ray images, age, gender, general physical conditions of the user etc. The invention is also based solely on non-invasive measurements methods.

The invention is used by a user (herein also referred to as "the user").

The invention may be applied to fingers, wrists, elbows, shoulders, back, hips, neck, knees, and ankles. The invention provides, at least potentially, the user to use his muscular activity in a manner where multiple repetitions of the same muscular activity is replaced by a diversity of muscular activities. Strength, duration and other parameters may be included in the method and system according to the invention.

In an example, the invention works by measuring, such as evaluation and/or recording, of muscular effect by, but not limited to, e.g. non-invasive SEMG (surface electro myography) measurements, see Figure 1. The invention converts this measuring to a signal that is, preferably, wirelessly sent to the user's smart device. The smart device can be a smart phone, tablet, computer, smart watch, but not limited to any of these. The signal is retrieved, transferred wirelessly, processed, and stored via an IT infrastructure and appropriate software. The software, preferably based on artificial intelligence, assesses the signal with respect to muscular power, number of repetitions, static work and time, but not limited to these. This assessment of the signal is processed and evaluated by the software into a near real-time feedback to the user, and the information is actively used to improve the basis for the wider feedback and stored for later potential statistical and medical research.

The sensor device may further be configured for being attached to a part of a user for sensing muscular activity(ies) in that part of a user during movement of said part and for providing a signal representative of the sensed muscular activity. The invention may further comprise a communication device in signal

communication with the sensor device, where the communication device comprises a detection unit, such that the detection unit physically forms part of the communication device.

The sensed muscular activity, used in the detection of a inexpedient muscular activity, may be classified using a signal, which may be an SEMG signal, originating from the sensor device and where the classification of the signal comprises the steps of

processing of the signal into a filtered signal, which may be a RMS signal, and

post- processing of the said signals, where the signals preferably are the filtered signal and said signal originating from the sensor device (the raw non-filtered signal originating from the sensor device), into statistical variables and/or functions relating to said signals and

use of the statistical variables and/or functions to classify and detect a sensed muscular activity by means of either an comparison to a database or by use of a trained machined learning algorithm or a combination thereof.

The statistical variables, used in the classification of the sensed muscular activity, may be chosen from the mean of the SEMG and/or the RMS, the standard deviation (STD) of the SEMG and/or RMS signal, the power of the SEMG and/or RMS signal, the time spent in a frequency category, e.g. each category has a certain range of frequencies, of the frequency categorized signal, the relative difference in the maximum amplitude of the signal compared to the baseline level, the relative power in different frequency bands and/or the peak amplitude in a given frequency interval.

The above analysis may be performed on a continuous signal which may be divided into appropriated time intervals or processed in a continuous manner. When the analysis is executed on a continuously signal the statistical functions may be evaluated on the basis of a changing time interval or by a comparison of statistical variables obtained in different time intervals. The invention may further comprise a communication device being spatial apart from the detection unit, the communication device being in signal communication with the sensor device and configured to

relay a signal received from the sensor device representative of a senses muscular activity to the detection unit, and

receive from the detection unit a signal signalling that a detection of an inexpedient muscular activity has been detected.

The detection unit may be configured for:

receiving signals from a plurality of sensor devices each signal being representative of a sensed muscular activity;

detecting, if present, in each of said received signals, one or more inexpedient muscular activities, and if detected,

tagging that specific signal(s) as representing inexpedient muscular activity and storing said tagged signal(s) in a database.

The detection of one or more inexpedient muscular activity may comprise:

receiving a signal representative of a sensed muscular activity;

comparing said received signal with the tagged signals stored in the database to find a possible match between the tagged signals and the received signal, and

if a match is found, preferably communicate that a match is found to the user.

The detection unit may be configured for detecting, if present, in the received signal, one or more inexpedient muscular activities, by comprising an artificial intelligent network.

In preferred embodiments the sensor device may be configured for wireless transmitting the signal representative of the sensed muscular activity.

The detection unit may be configured for wireless receiving the signal

representative of the sensed muscular activity and for wireless communicating that a detection of an inexpedient muscular activity has been detected. The communication device may be configured for wireless communication with said sensor device and said detection unit.

The detection unit may be embodied in a user's smart device such as a smartphone, tablet, computer, smart watch.

The communication to the user wearing the sensor device that an inexpedient muscular activity has been detected may be in the form of visual, auditory or tactile perceptive information.

In a preferred embodiment the sensor device may comprise

a tubular sleeve made from an elastic material

a device holding electronics

a socket attached to the sleeve and configured for receiving the device holding electronics, the socket comprises electrical conductive connections for the connecting with device holding electronics,

a number of sensor strips arranged at the inside of the sleeve, the sensor strips are connected to the connections provided in the socket so as to provide a connection between the sensor strips and the device holding electronics.

In a second aspect, the invention relates to a method for monitoring and preferably evaluating physical movements of a user, the method comprising :

detecting, if present, in said received signal, one or more inexpedient muscular activity(ies) (IMA), and if detected preferably communicate, e.g. to the user wearing the said sensor device , that an inexpedient muscular activity has been detected.

This aspect of the invention is particularly, but not exclusively, advantageous in that the present invention may be accomplished by a computer program product enabling a computer system to carry out the operations of the system of the first or the method according to the second aspect of the invention when down- or uploaded into the computer system . Such a computer program product may be provided on any kind of computer readable medium, or through a network.

Although the invention has been detailed with reference to measurement of SEMG, the invention may also be based, either alone or in combination with SEMG measurement, on measurement of 02 and/or C02 but not limited to this. Gyros and/or accelerometers may also by applied in connection with the present invention. The invention has the main advantageous that the measurements is non-invasive.

In a third aspect, the invention relates to a method for monitoring and preferably evaluating physical movements of a user, the method comprising :

on the basis of a signal obtained by a sensor device representative of a sensed muscular activity,

- detecting, if present, in said received signal, one or more

inexpedient muscular activities (IMA), and if detected preferably communicate, e.g. to the user wearing the said sensor device, that an inexpedient muscular activity has been detected.

The individual aspects of the present invention may each be combined with any of the other aspects. These and other aspects of the invention will be apparent from the following description with reference to the described embodiments.

BRIEF DESCRIPTION OF THE FIGURES

The present invention and in particular preferred embodiments thereof will now be described in more detail with regard to the accompanying figures. The figures show ways of implementing the present invention and are not to be construed as being limiting to other possible embodiments falling within the scope of the attached claim set.

Figure 1 schematically illustrates a first embodiment of a system according to the present invention according to which movement of an elbow is sensed, Figure 2 schematically illustrates a second embodiment of a system according to the present invention, Figure 3 schematically illustrates a third embodiment of a system according to the present invention,

Figure 4 is a photograph illustrating a user wearing a sensor device according to a preferred embodiment of the invention,

Figure 5 is a photograph illustrating the sensor device of fig. 4, in the figure the device holding electronics is shown detached from a socket,

Figure 6 is a photograph illustrating the device holding electronics of fig. s 4 and 5 and

Figure 7 is a photograph illustrating sensor device of fig. 4 as seen from reverse side.

Figure 8 is a flowchart detailing one embodiment of the invention detection method.

DETAILED DESCRIPTION OF AN EMBODIMENT

Reference is made to fig. 1 schematically illustrating an embodiment of a system according to the present invention. The system is configured for monitoring and evaluating physical movements of a user. Such monitoring and evaluation may be carried out while the user is working, resting, thus in general monitoring physical movement of the user irrespectively of whether or not the user is activating his or hers muscles. The system as illustrated comprising a detection unit 3 configured for receiving a signal obtained by a sensor device 1 representative of a sensed muscular activity. Such a detection unit may be computer, smart phone or the like and the signal may be received via wireless communication, such as a WIFI or Bluetooth connection. The signal received is typically a time wise stream of data, where each data point represents a muscular strain at a certain point in time. The detection unit 3 is configured for detecting, if present, in said received signal, one or more inexpedient muscular activities (IMA). The details as to how such an IMA is detected will be detailed below. If the detection unit 3 detects an IMA, the detection unit 3 communicate, e.g. to the user wearing the said sensor device 1, that a detection of an inexpedient muscular activity has been detected. It is noted, that while the detection unit is capable of detecting an IMA, the detection unit 3 does not as such give any measures that alleviate the IMA. However, it may be expected that the user will make a different use of his muscles in response to the communication that IMA has been detected.

As outlined, the detection unit 3 processes information received from a sensor device 1, and accordingly, the system may further comprising such sensor device

1. The sensor device 1 is typically configured for being attached to a part of a user for sensing muscular activity(s) in that part of a user during movement of said part and for providing a signal representative of the sensed muscular activity.

Such sensor device may be a sensor measuring a mechanical response of muscle activity but other sensor types may be used in connection with the present invention. The sensor device 1 further comprising - or is connected to - a transmitter, transmitting the sensed signal.

The signal from the sensor device 2 is to be received by the detection unit 3 and in some preferred embodiments, this data communication is handled by a communication device 2 forming part of the system. The communication device 2 is in signal communication 4 with said sensor device 1 for receiving data from the sensor device. Further, the communication device 2 may comprise detection unit 3, such as the detection unit 3 physically forms part of said communication device

2. In another configuration of the system - see fig. 2 - wherein the system

comprising a communication device 2, the communication device 2 is spatial apart from said detection unit 3. By spatial apart may typically mean that the detection unit 3 is at a different physical location, than the communication device, e.g. the detection communication device 2 may be a smart phone, and the detection unit 3 may be a centrally hosted server implementation. In such embodiments, the communication device 2 is in signal communication 4 with the sensor device 1 and is configured to relay a signal received from sensor device 1 representative of a senses muscular activity to the detection unit 3, and receive from the detection unit 3, a signal signalling that an inexpedient muscular activity has been detected.

As illustrated in fig. 3, another embodiment of the invention involves data from a plurality of users each wearing a sensor device 1. This is schematically illustrated in fig. 3 in which the detection unit 3 is illustrated as a cloud communicating with sensor device 1 (uploading) and a communication device 2 (not shown) arranged in vicinity of each user, that is in position allowing the user to recognize

information provided by the communication device 2. In such embodiments, the detection unit 3 is configured for receiving signals from a plurality of sensor devices 1, each signal being representative of a sensed muscular activity.

The detection unit 3 is in such embodiments configured for detecting, if present, in each of said received signals, one or more inexpedient muscular activities, and if this is detected, then tagging that specific signal(s) as representing inexpedient muscular activity and storing the tagged signal(s) in a database. This may be seen as a way of establishing a database on IMA signals, and such a tagging may be carried out in numeral ways - as inter alia described herein - and may involve a comparison of received signal with pre-selected signals representing IMA, where such pre-selection may be carried out manually by user. In embodiments where a pre-selection is used the comparison to conclude either positive or negative on IMA may be implemented as a signal being within certain limits of a pre-selected IMA is tagged to be an IMA signal.

If the detection unit 3 detects and inexpedient muscular activity, a corresponding signal may be transmitted to the communication device 2 of the user from which the specific signal is received.

In a further embodiment, the detection of one or more inexpedient muscular activity involves the steps of:

- receiving a signal representative of a sensed muscular activity; comparing said received signal with the tagged signals stored in the database to find a possible match between the tagged signals and the received signal, and

if a match is found, communicate that a match is found to the user.

A match is typically considered to be found if the received signal is within pre- selected limits of the tagged signals.

Alternatively, or in combination with the database look-up method disclosed above, the detection unit 3 may be configured for detecting, if present, in said received signal, one or more inexpedient muscular activities, by comprising an artificial intelligent network. A detailed explanation of this artificial intelligent network is presented below in the section labelled "AI-engine".

In order to make the system easy to use for the user, the sensor device 1 is configured for wireless transmitting said signal representative of the sensed muscular activity. Alternatively, the sensor device 1 may be hard-wired to the communication device 2 or detection device 3

Further, the detection unit 3 is advantageously configured for wireless receiving said signal representative of the sensed muscular activity and for wireless communicating that a detection of an inexpedient muscular activity has been detected.

Further, the communication device 2 is advantageously configured for wireless communication with said sensor device 1 and the detection unit 3.

Since many users today has a so-called smart device and uses that for other purposes, the detection unit 3 may be embodied in a user's smart device such as a smartphone, tablet, computer, smart watch. This can be provided by an application running on the smart device and has inter alia the advantage that such smart device typically contains the possibility to emit sound and/or light and vibrate which can be used to communicate certain information to the user as well as connectable to data sources being external to the smart device. The later may be used to configure the smart device to operate as a relay device, relaying information to a detection device 3 (as illustrated in fig. 3) and as a

communication device 2.

In particular preferred embodiments, the communication to the user wearing the sensor device 1 that a detection of an inexpedient muscular activity has been detected is in the form of visual, auditory or tactile (e.g. vibration) perceptive information.

As disclosed herein, the invention is capable of making measurements of the user's muscular effect via sensors. These measurements are typically transferred wireless to the application on, for instance, a smart phone, tablet etc. with the purpose of providing feedback in case the use of the muscle is inexpedient based on the measurement of for instance, but not limited to, muscular effect, number of repetitions, static work and time.

The detection of inexpedient muscular activity can be based on electromyography signals (EMG) originating from the sensor device 1. The EMG signals can be used to continuously classify the test subject's muscle activity (MA), either by use of a machine learning algorithm or by a database comparison. The muscle activity could be identified as length of contradiction, extension of muscle, flexion of muscle etc.

In one embodiment, in the classification of the muscle activity, used in the detection of an IMA, a machine learning algorithm is used. The machine learning algorithm is trained using variables and functions derived from the EMG signal and the root mean square (RMS) transformation of the signal. This RMS

transformation could be centred around a period of 201 samples, in the case of a 1043 Hz EMG signal, thereby

RMS(i ) =

·

The learning and detection variables can be chosen as statistical functions and variables based on the EMG and RMS signal. These can be chosen, but not limited to, the mean of the EMG and the RMS, the standard deviation (STD) of the EMG and the RMS, the power of the EMG and RMS, the time spent in a frequency category of the frequency categorized signal, the relative difference in the maximum amplitude of the signal compared to the baseline level, the relative power in different frequency bands, the peak amplitude in a given frequency interval etc.

The training of the machine learning algorithm could be implemented using a communication device 2. The communication device 2 would instruct the user to perform an exercise, thereby enabling a direct classification of the muscular activity. The training stage could comprise two stages, a warm up stage and a specific training stage where the algorithm is trained. The test could, in one embodiment, be performed as 3 session with 10 repetition of each exercise. The device could then automatically choose 10 second intervals where the annotation of the repetition is present and discard measurement where 10 second

measurements is not present. Other suitable time intervals could also be chosen. The machine learning algorithm could, in one embodiment, be constructed using 61 nodes and 31 terminals. In one scenario the algorithm could detect hand contradiction with a predefined resistance, by identifying that if the RMS signal did not show constant activity, if the low frequency peak is over 2% of the total power, the spread in RMS signal is high, the relative power in the 1-49 Hz bandwidth is under 11 and the frequency stop in the 0.3-5 Hz bandwidth is under 0.6 Hz. The output of the machine learning algorithm is a muscle activity which can be used with the AI - engine describe below. The process can symbolically be written as

MA_t = HJEMG. RMS,

where MA is a specific muscle activity and the subscript i indicates the location of the sensor device. Here will the function H evaluate the MA activity based on the post- processing of the EMG and RMS signal. The function, H, can be implemented as a machine learned algorithm where the network is trained as mentioned above.

As describe above H is a continuously evaluated function, which will automatically divide the EMG and RMS signal into suitable intervals so the MA activities can be continuously evaluated and used in the inexpedient muscular activity detection. The function, H, would after the training stage be able to detect muscular activity in the test subject, based on the same statistical variables and functions used in the training stage. In the above embodiment of the muscular activity detection system the

classification of the activity can in parallel be classified by use of comparison of the data to a database. The function, H, will in this scenario represent a direct comparison to a database comprising data about, but not limiting to, the test subject age, physical attributes etc.

In should also be noted that in the above embodiment the classification of the muscular activities is achieved by using statistical functions and features of time defined intervals of the sensor signal but this should not be view as a limiting factor. The method also works with a continuously sensor signal as input to the detection method. The method and device could also be used to detect other external physiological changes, such as shaking etc.

AI - engine

As disclosed herein, the invention comprises in some embodiments, a detection unit being configured for detecting, if present, one or more expedient muscular activities by use of an artificial intelligent network (for short AI). In the following, the build and use of the AI will be disclosed.

The build and use of AI may be disclosed as occurring in four steps.

Step 1 : A number of pre-coded rules are developed in a first step and optionally user characteristics, such as age and/or gender. The pre-coded rules are based on a pre-recognition (typically in a manually manner) of inexpedient muscular activity (IMA) having as parameters e.g. number of muscular contractions (NMC), duration of muscular contractions (DMC) and the strength of the muscular contractions (SMC). This may symbolically be written as

where index / indicates that the IMA is considered for a specific body part, e.g. the wrist. Index / indicates a particular inexpedient muscular activity, and is used in case more than one muscular activity is considered to be inexpedient for the particular body part. Superscript F indicates pre-coded rules (function of pre- coded rules). in the above indicates that other parameters may be taken into consideration. Thus, this step may be seen as calibrating the function F.

A pre-coded rule may be understood in the following manner. Consider a sensed muscular activity described by NMC, DMC, SMC (but necessarily limited thereto). When evaluated by the function Fj a muscular activity MA is determined by the function F and if F(MA)=IMA then the muscular activity is tagged to be

inexpedient. Please note, that the function F may often be composed of algebraic operators and that the equality operator in F(MA)=IMA may be evaluated as being within certain numerical limits, e.g. ±5%.

Step 2

Muscular activity data e.g. NMC, DMC, SMC is collected from a plurality of users and the data collected is stored in a database together with the users' own registration of the experienced pain, typically on a scale between 1-10, where 1 represent no-pain and 10 represent highest pain. Machine learning is executed on the stored data and the learning is based on a number of registered patterns known to represent users' pain experience and thus assumed to represent inexpedient muscular activity. These registered patterns (often manually registered as inexpedient muscular activities) may be considered as the rules on which the machine learning develops, to provide an artificial intelligence based algorithm which based on new data can determine the data to be inexpedient or not. Accordingly, the artificial intelligence based algorithm may be considered to be machine learned rules, G, e.g. expressed symbolically as

IMAf_j if G_i:j(NMC, DMC, SMC, ... ) > TH, where TH is a threshold set by the system or the user. The machine learning function G produces a likelihood of IMA between 0 and 100%, where 0% represent no likelihood of IMA and 100% represent highest likelihood of IMA.

In another embodiment, the function G includes the threshold, so that the function G only provides a result expressed as either "true" or "false". The threshold TH is typically a tunable variable in the sense that e.g. an administrator (user) may set the value as desired.

Suitable tools for this machine learning/artificial intelligence have found to be Azure Machine Learning, R or the like.

In an alternative to using the registered patterns as rules, the pre-coded rules, F, (see above) may be used as rules or a combination thereof may be used.

Step 3:

Step 3 may be considered as the step during which an inexpedient muscular activity is detected, if present, in data received from a user. Please also refer to figures 1-3. The artificial intelligence and the pre-coded rules are accessible by the detection unit 3 and muscular activity data obtained by a user wearing the sensor device 1 is received by the detection unit 3.

Thus, as an non-limiting example a received muscular activity MA=(NMC, DMC, SMC) is input to the machine learning rules G the result of which provides a value (percentage or "true" "false") for 1MA^G . The received muscular activity MA is also input to the pre-coded rules F(MA)\ G(MA ) e [true; false] and F(MA ) e [IMA; ¹ IMA]

IMA is predicted to have been detected in case of

G(MA ) = true OR F(MA ) = true THEN IMA = true

This may be considered as a fail-safe set-up. In an alternative embodiment, both rules must detect an IMA in order for the IMA to be set to be true: G(MA ) = true AND F(MA ) = true THEN IMA = true

Here IMA = true means that an inexpedient muscular activity has been detected. This detection is communicated to the user from which the data is obtained.

Depending on the complexity of the invention, the communication to the user may be limited to an information that the inexpedient muscular activity has been detected or include further information as to suggested measures to be taken by the user to avoid such inexpedient muscular activity in the future.

Step 3 may be seen as also comprising a data collection which may data advantageously be used in the training of the artificial intelligence as disclosed above in relation to step 2.

In further embodiments, the invention could be used to detect other physiological and behavioral conditions, which is not necessarily an inexpedient muscular activity. This could, but is not limited to, the following list

Pregnancy, the system could be adapted to predict and classify situations related to labor. This could be the measurements of the length (duration) and severity of labor contractions.

Physical rehabilitation, the system could assist in activation of the, in the situation, required muscle and also assist in the activation intensity and length. The physical rehabilitation could, but is not limited to, stem from an operation or medical/physiological condition or procedure.

Physical training optimization, the system could be adapted to be used in a physical training optimization. The system could inform the user about which movements should be improved on and predict possible injuries. This could be used in an elite and recreational sports setting.

Development of consumer goods, the system could be adapted to be used in the development stages of consumer goods. The system could provide information about the human ergonomics in connection with the product. This could be in the case of IT-equipment, furniture, cars, chairs, etc.

- Calculation of insurance premiums, the system could be adapted to be used in the insurance industry where it could provide information about the extent of an injury. It could also be used as basis for calculating the insurance premium for a certain demographical and social group by use of the data collected from the devices.

- Activation of people, the invention could be adapted to assist people in becoming more active in their daily life. This could be achieved by prompting the user after a period of inactivity. It could provide a report detailing when the user is inactive during the day and further provide suggestions for activities and time intervals for those activities. This could be helpful for kids, elderly people and people who has a sedentary occupation.

- Gaming prediction, the system could be adapted to predict when a test subject is playing games. This could be computer or console based games. Parental control, the system could be used in connection with the gaming prediction tool to be able to predict if the person is playing video games and if so alert the parental user. If could be used to detect other activities, which the parental user, would like to receive information about.

Predict fainting event, the system could be adapted to alert the user if they are about to faint. This could be in case of a medical condition. The device could predict the fainting event ahead of time and alert the user, so the user can take precautions.

Neurological deceases, the system could detect physiological indications for neurological seizures. This could be shaking and other indications for seizures and alert the user.

Stress, the system could be adapted to detect conditions relating to stress.

A common feature in all of the further embodiment is the feedback system to the user and the sensor device capable of receiving general external physiological conditions. The invention can be implemented in situations where an external physiological change occurs in the test subject. The above mention scenarios is not an exhausted list and the invention could be utilized in other application avenues. See figure 8, for a flowchart detailing the process for one embodiment of the invention, detailing the process from detection of physiological change to detection of activity.

The invention may be characterised by being independent of professionals instructing the user, and can be used by the user without instruction, which will save time and expensive consultation. The invention is intuitive in use and does not require extra equipment as previous solutions, making it more accessible to the user, so it can be integrated into the user's everyday life. The user is therefore expected to use the invention more frequently and the use will have a more lasting effect. The invention is based on measurements of several parameters that are collected from the sensors, which are sent wirelessly to the user's smart device, where the signal is processed through algorithms to ultimately give the user feedback on, for example, a potential overload of a muscle, or feedback to corrective measure.

The invention can be implemented by means of hardware, software, firmware or any combination of these. The invention or some of the features thereof can also be implemented as software running on one or more data processors and/or digital signal processors.

The individual elements of an embodiment of the invention may be physically, functionally and logically implemented in any suitable way such as in a single unit, in a plurality of units or as part of separate functional units. The invention may be implemented in a single unit, or be both physically and functionally distributed between different units and processors.

Reference is now made to figures 4-7, whereof figure 4 is a photograph

illustrating a user wearing a sensor device 1 according to a preferred embodiment of the invention. The sensor device comprising a tubular sleeve 6 made from an elastic material, such as an elastic fabric allowing it to fit firmly on the user's arm as illustrated and allowing a user to pass his hand and arm through interior of sleeve when the sensor device 1 is to be arranged on the arm. As also illustrated in fig. 4, the user carries a smartphone which may be configured (as other-wise disclosed herein) to operate as a communication device and/or detection device.

Although the sensor device 1 is illustrated as being used on an arm and elbow, the same principle may and in some instances, the same device 1, may be used on different body parts.

With reference to fig. 5, which is a photograph illustrating the sensor device 1 of fig. 4. As illustrated, the sensor device comprises a device holding electronics 8. Such electronics are preferably the electronic components used for converting the sensed signal into data and transmitting such data to the communication device and/or the detection unit 3. The electronic components may also include data memory, data processor, battery(ies), accelerometer and/or gyros. In fig. 5 the device holding electronics 8 is shown detached from a socket 7 which is attached to the sleeve 6. The socket also comprises conductive (typical electrically conductive) connections 9 for connecting with the device holding electronics 8, the purpose of which will be disclosed on connection with the 7. The socket 7 is adapted to receive the device holding electronics 8 in a firm fit preventing the device 8 to fall out of the socket 7 during the user wearing the sensor device 1. The device holding electronics 8 comprising connections (not illustrated) mating the connections 9 once the device 8 is arranged in the socket to allowing electrical connections between the device holding electronics 8 and the connections 9.

By use of a socket 7, such a socket may be applied to different shaped sleeves 6, whereby different sleeves 6 may be used to accommodate the same device holding electronics 8.

In figure 6 the device holding electronics of fig.s 4 and 5 is illustrated as a photograph. Fig. 6 illustrates inter alia that the device holding electronics may comprises a USB connection 12 allowing access to data stored in the device, programming of data processors (if present) and/or charging rechargeable battery(ies), if present.

Reverting now to figure 7 which is a photograph illustrating the sensor device 1 of fig. 4 as seen from reverse side. By reverse side means that the sleeve 6 has been turned inside-out revealing the inner surface of the sleeve 6 which during use abut the skin of the user. As illustrated in fig. 7, a number of sensor strips 10, such electrical sensor strips is arranged at the inside of the sleeve 6; in the embodiment of fig. 7, three such sensor strips 10 are arranged. The sensor strips 10 may be arranged parallel to each other. The sensor strips 10 are connected to the connections 9 provided in the socket 7 so as to provide a connection between the sensor strips 10 and the device holding electronics 8.

In figure 8 an flow-diagram over an embodiment of the invention is presented. A continuous measurement from the sensor device 1 is received in the form of a EMG signal. The EMG signal is separated into suitable intervals. These intervals could be chosen to be 10 second, but other intervals may be used. The EMG signal is then processed into a RMS signal, which together with the original EMG signal is post processed into suitable statistical features. These features could be time spent in a frequency categorized signal, the relative power in different frequency bands etc.

The physiological activity is then detected, using the statistical features from the RMS and EMG signal, by using either a trained machine learning algorithm or the comparison to a database or combination thereof. The physiological activity, such as a muscular activity, is used in the detection of the inexpedient muscular activity, by using information such as duration, type of activity etc. from the previous analysis. The inexpedient muscular activity is detected by using either a machine learning algorithm, a comparison with a database or a fail-safe method (or even combination thereof), where a comparison between the database and machine learning prediction is carried out.

In some embodiments of the invention the user of the device is prompted with the result of the analysis. This could be accompanied with suggestions for alleviation and prevention.

Although the present disclosure has been focused on the use on human beings, the invention is not limited to humans and may be used on animals, preferably larger animals exhibiting muscular activities measurable to obtain EMG signals, such as dogs, cats, horses, cattle, goats, pigs, cows, sheep etc.

Although the present invention has been described in connection with the specified embodiments, it should not be construed as being in any way limited to the presented examples. The scope of the present invention is to be interpreted in the light of the accompanying claim set. In the context of the claims, the terms "comprising" or "comprises" do not exclude other possible elements or steps. Also, the mentioning of references such as "a" or "an" etc. should not be construed as excluding a plurality. The use of reference signs in the claims with respect to elements indicated in the figures shall also not be construed as limiting the scope of the invention. Furthermore, individual features mentioned in different claims, may possibly be advantageously combined, and the mentioning of these features in different claims does not exclude that a combination of features is not possible and advantageous. 1 Sensor device

2 Communication device

3 Detection unit

4 Data communication

5 Transmission

6 Sleeve

7 Socket

8 Device holding electronics

9 Connections (for device holding electronics) 10 Sensor strips

11 Smartphone

12 USB-connection

Claims

1. A system for monitoring movements of a user, the system comprising :

a detection unit (3) configured for

- receiving a signal obtained by a sensor device (1) representative of a sensed muscular activity,

detecting, if present, in said received signal, one or more inexpedient muscular activity(ies) (IMA), and if detected.

2. A system according to claim 1, further comprising the step of communicating, e.g. to the user wearing the said sensor device (1), that an inexpedient muscular activity has been detected

3. A system according to claim 1, further comprising said sensor device (1) configured for being attached to a part of a user for sensing muscular activity(ies) in that part of a user during movement of said part and for providing a signal representative of the sensed muscular activity.

4. A system according to any of the preceding claims, wherein the system further comprising a communication device (2) in signal communication (4) with said sensor device (1), said communication device (2) comprising said detection unit (3), such as said detection unit (3) physically forms part of said communication device (2).

5. A system according to any of the preceding claims, where the sensed muscular activity is classified using a signal, preferably an EMG signal, originating from the sensor device (1) and where the classification of the signal comprises the steps of processing of the signal into a filtered signal, preferably into an RMS signal, and

- post- processing of the said signals into statistical variables and/or

functions relating to said signals and

use of the statistical variables and/or functions to classify and detect a sensed muscular activity by means of either an comparison to a database or by use of a trained machined learning algorithm, or a combination thereof.

6. A system according to claim 5, where the statistical variables are chosen from the mean of the SEMG and/or the RMS, the standard deviation (STD) of the EMG and/or RMS signal, the power of the EMG and/or RMS, the time spent in a frequency category of a the frequency categorized signal, the relative difference in the maximum amplitude of the signal compared to the baseline level, the relative power in different frequency bands and/or the peak amplitude in a given frequency interval.

7. A system according to any of the preceding claims, wherein the system further comprising a communication device (2) being spatial apart from said detection unit (3), the communication device (2) being in signal communication (4) with said sensor device (1) and configured to

relay a signal received from said sensor device (1) representative of a senses muscular activity to the detection unit (3), and

receive from the detection unit (3) a signal signalling that a detection of an inexpedient muscular activity has been detected.

8. A system according to any of the preceding claims, wherein the detection unit (3) is configured for:

receiving signals from a plurality of sensor devices (1) each signal being representative of a sensed muscular activity;

- tagging that specific signal(s) as representing inexpedient muscular activity and storing said tagged signal(s) in a database.

9. A system according to claim 8, wherein the detection of one or more

inexpedient muscular activity comprising :

- receiving a signal representative of a sensed muscular activity;

if a match is found, preferably communicate that a match is found to the user.

10. A system according to any of the preceding claims, wherein the detection unit (3) is configured for detecting, if present, in said received signal, one or more inexpedient muscular activities, by comprising an artificial intelligent network.

11. A system according to any of the preceding claims, wherein the sensor device (1) is configured for wireless transmitting said signal representative of the sensed muscular activity.

12. A system according to any of the preceding claims, wherein the detection unit (3) is configured for wireless receiving said signal representative of the sensed muscular activity and for wireless communicating that a detection of an inexpedient muscular activity has been detected.

13. A system according to any of the preceding claims, wherein communication device (2) is configured for wireless communication with said sensor device (1) and said detection unit (3).

14. A system according to any of the preceding claims, wherein the detection unit (3) is embodied in a user's smart device such as a smartphone, tablet, computer, smart watch.

15. A system according to any of the preceding claims, wherein the

communication to the user wearing the sensor device (1) that an inexpedient muscular activity has been detected is in the form of visual, auditory or tactile perceptive information.

16. A system according to any of the preceding claims, wherein the sensor device (1) comprises

- a tubular sleeve (6) made from an elastic material

a device holding electronics (8)

a socket (7) attached to the sleeve (6) and configured for receiving the device holding electronics (8), the socket comprises electrical conductive connections (9) for the connecting with device holding electronics (8), a number of sensor strips (10) arranged at the inside of the sleeve (6), the sensor strips (10) are connected to the connections (9) provided in the socket (7) so as to provide a connection between the sensor strips (10) and the device holding electronics (8).

17. A method for monitoring and, preferably, evaluating physical movements of a user, the method comprising :

receiving a signal obtained by a sensor device (1) representative of a sensed muscular activity,

- detecting, if present, in said received signal, one or more

inexpedient muscular activity(ies) (IMA), and if detected preferably communicate, e.g. to the user wearing the said sensor device (1), that an inexpedient muscular activity has been detected.

18. A method for monitoring and, preferably, evaluating physical movements of a user, the method comprising :

on the basis of a signal obtained by a sensor device (1) representative of a sensed muscular activity,

detecting, if present, in said received signal, one or more inexpedient muscular activities (IMA), and if detected

preferably, communicate, e.g. to the user wearing the said sensor device (1), that an inexpedient muscular activity has been detected..