EP3163545A1 - Détection d'une activité anormale pour personnes âgées et personnes handicapées - Google Patents

Détection d'une activité anormale pour personnes âgées et personnes handicapées Download PDF

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Publication number
EP3163545A1
EP3163545A1 EP15306725.1A EP15306725A EP3163545A1 EP 3163545 A1 EP3163545 A1 EP 3163545A1 EP 15306725 A EP15306725 A EP 15306725A EP 3163545 A1 EP3163545 A1 EP 3163545A1
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Prior art keywords
user
user location
cluster
time
location sequence
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EP15306725.1A
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German (de)
English (en)
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Eric Gautier
Serge Defrance
Françoise Le Bolzer
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Thomson Licensing SAS
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Thomson Licensing SAS
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    • G—PHYSICS
    • G08—SIGNALLING
    • G08B—SIGNALLING OR CALLING SYSTEMS; ORDER TELEGRAPHS; ALARM SYSTEMS
    • G08B21/00—Alarms responsive to a single specified undesired or abnormal condition and not otherwise provided for
    • G08B21/02—Alarms for ensuring the safety of persons
    • G08B21/04—Alarms for ensuring the safety of persons responsive to non-activity, e.g. of elderly persons
    • G08B21/0407—Alarms for ensuring the safety of persons responsive to non-activity, e.g. of elderly persons based on behaviour analysis
    • G08B21/0423—Alarms for ensuring the safety of persons responsive to non-activity, e.g. of elderly persons based on behaviour analysis detecting deviation from an expected pattern of behaviour or schedule
    • G—PHYSICS
    • G08—SIGNALLING
    • G08B—SIGNALLING OR CALLING SYSTEMS; ORDER TELEGRAPHS; ALARM SYSTEMS
    • G08B21/00—Alarms responsive to a single specified undesired or abnormal condition and not otherwise provided for
    • G08B21/02—Alarms for ensuring the safety of persons
    • G08B21/04—Alarms for ensuring the safety of persons responsive to non-activity, e.g. of elderly persons
    • G08B21/0438—Sensor means for detecting
    • G08B21/0469—Presence detectors to detect unsafe condition, e.g. infrared sensor, microphone

Definitions

  • the proposed method and apparatus relates to the detection of abnormal activities of elderly and handicapped people residing in their homes or non-medical residences.
  • LCS Longest Common Subsequence
  • the LCS similarity measure is commonly used to measure similarity between DNA sequences. This definition can be extended to define symbolic temporal sequences, describing the successions of sensors that are triggered. Typically, having n sensors, one symbol may be used to describe the position of the n sensors. The symbol could be a n-digit number, each digit being represented by a 0 or 1 which is the current position of one sensor.
  • FIG. 1 is an example of the "User Motion Sequence" obtained from time-stamped events representing motion sensor activations or de-activations.
  • the proposed method and apparatus concerns the implementation of an abnormal activity detection technique for elderly and handicapped people. While principally directed to detecting anomalous behavior of elderly or handicapped individuals, the proposed method and apparatus are not so limited and may be employed to monitor prisoners, children or other individuals that find themselves in restricted environments.
  • the description of the proposed method and apparatus uses elderly or handicapped individuals as examples.
  • the data collection system In order to be accepted by elderly and handicapped individuals and not disturb them, the data collection system must be as non-intrusive as possible. Data is collected only from a minimal number of motion and doors activation sensors, which have been judiciously placed in the residence of the elderly or handicapped individual. With such limited collected data, usual anomaly detection techniques such as similarity-based techniques of temporal sequences do not perform correctly because sequences indicating sensor activations present very sparse information.
  • the proposed method and apparatus extends the features extracted from data collected from motion and door activation sensors by producing features indicating the location of the person in her home or outside her home, in order to be applicable efficiently in the case of anomaly detection using similarity-based techniques on the temporal sequences.
  • the proposed method and apparatus use the non-intrusive approach to detect outliers even if the model does not have any information or knowledge as to whether the individual is sitting, standing, walking, etc.
  • the proposed method and apparatus categorizes a user's life routines from sensory data provided during several days by using a clustering technique and testing whether a new day of sensory measurements is an outlier or not in line with the categorized and clustered routines.
  • the user is the elderly or handicapped individual who is the subject of the anomaly detection.
  • Clustering is used because it is an unsupervised machine learning method enabling grouping of similar objects into respective categories according a similarity (i.e. distance) measure definition. The principle is to cluster the points of the data set and to determine the distance between a new data point and the medoids, which represent the clusters. Distances above a certain threshold are outliers.
  • a method and apparatus for creating an anomalous behavior detection log for reporting anomalous behavior of a user in an environment including logging first time-stamped event data of initial behavior of the user over a period of time in the environment, estimating a location of the user in the environment using the time-stamped event data of the initial behavior of the user, creating a characterization of life routines of the user's initial behavior, logging second time-stamped event data of subsequent behavior of the user, estimating a user location sequence using the second time-stamped event data of the user's subsequent behavior, comparing the characterization of life routines of the user's initial behavior and the estimated user location sequence of the user's subsequent behavior and creating an anomalous behavior detection log by detecting anomalous behavior using the user location sequence comparisons.
  • processor or “controller” should not be construed to refer exclusively to hardware capable of executing software, and may implicitly include, without limitation, digital signal processor (DSP) hardware, read only memory (ROM) for storing software, random access memory (RAM), and nonvolatile storage.
  • DSP digital signal processor
  • ROM read only memory
  • RAM random access memory
  • any switches shown in the figures are conceptual only. Their function may be carried out through the operation of program logic, through dedicated logic, through the interaction of program control and dedicated logic, or even manually, the particular technique being selectable by the implementer as more specifically understood from the context.
  • any element expressed as a means for performing a specified function is intended to encompass any way of performing that function including, for example, a) a combination of circuit elements that performs that function or b) software in any form, including, therefore, firmware, microcode or the like, combined with appropriate circuitry for executing that software to perform the function.
  • the disclosure as defined by such claims resides in the fact that the functionalities provided by the various recited means are combined and brought together in the manner which the claims call for. It is thus regarded that any means that can provide those functionalities are equivalent to those shown herein.
  • a proposed method and apparatus for anomaly detection for elderly and handicapped people can be roughly broken down into three main steps as shown in Fig. 2 and explained in detail below.
  • the motion sensors and door contacts are used to collect and time stamp event data regarding the location of the elderly or handicapped individual in their residence.
  • This collected time-stamped event data of a user's initial behavior over a period of time in the environment is logged (recorded) by the server.
  • the environment for an elderly or handicapped user is the user's home or non-medical residence.
  • the environment for an individual such as a prisoner or child will be different.
  • the collected time-stamped event data of the user's initial behavior is forwarded to a server where the collected time-stamped event data is used to estimate the location of the user in their environment.
  • the server uses the estimation of location of the user to characterize the user's life routines.
  • New time-stamped event data representing a user's subsequent behavior is collected and forwarded to the server where the new time-stamped event data is logged.
  • the server uses the new time-stamped event data to estimate a user location sequence of the user in their environment.
  • the newly collected time-stamped event data is compared against the user's life routines to detect anomalous behavior.
  • comparisons of the sequences of the user's initial life routines against the estimated user's location sequence are generated.
  • An anomalous behavior detection log is created based on the comparisons of the sequences of the user's initial life routines and the estimated user location sequence.
  • the anomalous behavior detection log is used to alert a care giver of the anomalous behavior.
  • the care giver may be a nurse, a doctor, an aide, a family member etc.
  • a care giver may be a person who monitors the activities of the individual being monitored.
  • the alert may be by phone, by email, by text message or any other reasonable means.
  • Fig. 3 shows an exemplary case, where one individual is living in a residence equipped with a non-intrusive data collection infrastructure and where each main room is covered by one omni-directional motion detector installed on the ceiling.
  • These main rooms are labeled the “bedroom”, the “living”, the “kitchen”, the “bedroom2”, the “office” and the corresponding motion sensors are respectively indicated as “mB”, “mL”, “mK”, “mB2", “mO”.
  • one external "door” is equipped with a door contact indicated as "cD”.
  • a representation of a time-stamped event includes listing the events corresponding to the sensor activations together with the date and time at which the events occurred.
  • the symbolic temporal sequences built directly from motion and door sensors are not used.
  • the symbolic temporal sequences deliver sparse information and lead to poor results.
  • the time-stamped event representation is used to build a one day collection of temporal sequences of symbolic states representing an estimation of the location of the individual in each room of his home or outside his home during the data collection period. The duration of these sequences can be adapted to the problem to be resolved.
  • one day collection sequences limited to nighttime hours (from 10pm to 9am) only can be considered.
  • the location of an individual in a room depends on the sensor activation and where the individual was located initially.
  • the sensor deactivation is not taken into account. For example, suppose that the individual is located in the bedroom, so that as soon as the motion sensor located in the living room is activated, it can be estimated that the individual has relocated to the living room. The same presence estimation can be applied to the other rooms. In case the individual is located in a room and the door is open, it is assumed that the individual has gone outside. Inversely when the individual is outside and the door is activated, it is assumed that the individual has moved inside. A motion sensor should be activated in a room to locate him in the corresponding room.
  • Fig. 4 illustrates an example of user location sequence provided by time stamped events sequences of living and bedroom motion sensors.
  • the proposed method and apparatus can make some location estimation errors particularly regarding the result of door activation. Indeed an individual located inside the home can open and close the door without going outside. To limit the consequence of such a condition, the motion sensors are given a priority over the door activation sensors. In that case, as soon as a motion sensor is activated in a room, the individual is deemed to be located in the corresponding room even if he was initially located outside. So this estimation error is limited to a short period of time, corresponding to the duration the individual is not detected as moving within a specific room after entering it in his house. Inversely, when an individual is located outside and activates the door without entering in the home, he will be nevertheless located inside. To limit the impact of this kind of error, when the individual is inside without activation of a motion sensor in a specific room beyond a duration threshold, the individual is deemed to be outside.
  • motion sensors could be replaced by door activation sensors in rooms where people usually open and close the door when they use it. This is generally the case for the front door, back door or restroom door for instance.
  • Table 1 An example of the results of the proposed location estimation method and apparatus applied to the area of elderly and handicapped care is shown in Table 1 below.
  • the leftmost column of Table 1 indicates the initial location of the person and the first row the motion or door sensor activations. The resulting location is then given in each table cell.
  • temporal symbolic sequences enable exploiting other kind of features based on the location of the individual in the home, like time spent in a specific room or outside the home, number of times the individual stays in a specific room, hour he enters the room and duration he stays in it, etc...
  • Fig. 5 is an exemplary plot of 15 sequences (y_axis) corresponding to 15 days of sensory measures and representing the location of that person in her home between 0 to 24h (x axis). Each color represents the location of the individual in a room as indicated in the legend.
  • the individual's "life routines" are characterized as a learning process by applying an unsupervised clustering process on a set of learning sequences using the length of the Longest Common Subsequence (LCS) as a similarity measure.
  • LCS Longest Common Subsequence
  • the LCS is a popular sequence similarity measure used in biology to measure similarity between DNA sequences. It is defined as follows: given two sequences X and Z, Z is a subsequence of X if removing some symbols from X will produce Z and Z is a common subsequence of two sequences X and Y if Z is a subsequence of X and Y.
  • LCS MJAU
  • length of LCS 4.
  • the length of the LCS is a very effective measure because it measures similarity between two sequences without restricting itself to a location-based one to one match.
  • clusters that are considered as significant of the individual's life routines are identified and selected. Indeed a cluster with few sequences with respect to the number of sequences of the learning set, cannot be considered as a user life routine. So a threshold is applied to retain only the clusters having at least a significant number of sequences.
  • a cluster medoid (or simply medoid) is a sequence whose average dissimilarity to all the sequences in the cluster is minimal. A medoid is chosen as representative of each significant cluster.
  • anomaly detection is performed on a new sequence of time stamped events corresponding to the activation/deactivation of motion or door sensors.
  • First of all the new sequence of time stamped events to be processed is transformed into a user location sequence as described above.
  • the anomaly detection process is achieved by determining whether this sequence is considered as a user's life routines or not. So for each significant cluster, the LCS distance between the current (new) processed sequence and the representative of the cluster (such as the cluster medoid) is calculated. This distance is compared with the greatest LCS distance between the medoid and the sequences of the cluster. If the former is greater than the latter the current (new) sequence is considered as anomalous (see Fig. 6 ). If the former is less than the latter, then the current (new) sequence is not considered as anomaly.
  • Fig. 7 is a flowchart of an exemplary implementation of step 205 of Fig. 2 (estimation of the user's location).
  • the time-stamped event data of the sensors is recorded for a pre-determined period of time. The period of time may be two weeks or more.
  • the time-stamped event data is transmitted to a server for recordation (storage).
  • the motion sensors and door contacts are not necessarily smart devices and they do not have sufficient processing power to perform the proposed method, so the time-stamped event data is transmitted to a server for recordation (storage) and processing.
  • the time-stamped event data collected for the pre-determined period of time is to be used as a training set (training data, learning data).
  • the server creates user location sequences from the time-stamped event data which are used to creating a characterization of life routines of the initial behavior of the user.
  • Fig. 8 is a flowchart of an exemplary implementation of step 210 of Fig. 2 (characterization of user's life routines).
  • the server creates a collection of the user location sequences. The successive individual user location sequences created at 710 across a period of time (2 weeks for example), are collected together and will be used to characterize the user's life routine at 805 (learning/training).
  • the server applies an unsupervised clustering process to the user location sequences to identify clusters of the user location sequences.
  • the clustering process uses a similarity measure such as LCS for example. Some other similarity measure such as the "optimal matching distance" can also be considered.
  • the server identifies significant clusters from among the identified clusters.
  • the server selects a medoid to represent each significant cluster.
  • a medoid is the user location sequence in a cluster (significant cluster) whose dissimilarity to all other user location sequences in the cluster is minimal.
  • the server calculates a distance between the medoid representing the cluster and each user location sequence in the cluster. This is done for each significant cluster.
  • the server determines a greatest distance from among the calculated distances between the medoid of a cluster and each user location sequence of the cluster. This is performed for each significant cluster.
  • Fig. 9 is a flowchart of an exemplary implementation of step 215 of Fig. 2 (detect anomalous behavior).
  • the server calculates a distance between the medoids of each significant cluster and the new user location sequence.
  • the server compares the distance between (the medoids of each significant cluster and the new user location sequence) and (the greatest distances between the medoids of each cluster and each user location sequence of each cluster). This is performed for each significant cluster.
  • a test is performed to determine if the new user location sequence is within the user's (normal) life routine. If the new user location sequence is not within the user's (normal) life routine then the new user location sequence represents anomalous behavior and an anomalous behavior detection log is created.
  • the distance between the medoids of each significant cluster and the new user location sequence is greater than the greatest distances between the medoids of each cluster and each user location sequence of each cluster, then there is anomalous behavior (the new user location sequence is not within the user's (normal) life routine).
  • Fig. 10 is a block diagram of an exemplary server that performs the proposed method.
  • the motion sensors and door contacts provide input to a record data module of the estimate location module. This is time-stamped event data and is recorded for a period of time. The period of time may be two weeks or more.
  • the time-stamped event data is transmitted to a server for recordation (storage) by the motion sensors and door contacts.
  • the motion sensors and door contacts are not necessarily smart devices and they do not have sufficient processing power to perform the proposed method so the time-stamped event data is transmitted to a server for recordation (storage) and processing.
  • the time-stamped event data collected for the pre-determined period of time is to be used as a training set (training data, learning data).
  • the create user location sequence module of the estimate location module creates user location sequences from the time-stamped event data.
  • the estimate location module logs first time-stamped event data of initial behavior of the user over a period of time (for example, two weeks) in the user's environment.
  • the characterize user life routine module creates a characterization of life routines of the user's initial behavior.
  • the estimate location module logs second time-stamped event data of subsequent behavior of the user.
  • the detect anomalies module generates user location sequence comparisons by comparing the characterization of life routines of the user's initial behavior and the estimated user location sequence.
  • the detect anomalies module then creates an anomalous behavior detection log by detecting anomalous behavior using the user location sequence comparisons.
  • the create collection of user location sequence module collects individual user location sequences.
  • the collection of user location sequences is used to characterize the user's life routine in the learning/training step (805).
  • the apply clustering process module of the characterize user life routine module receives input from the create collection of the user location sequence module, which creates a set of user location sequences for the period of time (2 weeks for example).
  • the apply clustering process module applies an unsupervised clustering process to the collection of the user location sequences to identify clusters of the user location sequences.
  • the clustering process uses a similarity measure such as LCS.
  • the identify significant clusters module then identifies significant clusters from among the identified clusters. This is accomplished by comparing a number of user location sequences in each of the identified clusters to a threshold.
  • the select medoid module selects a medoid to represent each significant cluster.
  • a medoid is the user location sequence whose dissimilarity to all other user location sequences in the cluster is minimal.
  • the calculate distance module then calculates a distance between the medoid representing the cluster and each user location sequence in the cluster. This is done for each significant cluster.
  • the calculate greatest distance module then determines a greatest distance from among the calculated distances between the medoid of a cluster and each user location sequence of the cluster. This is performed for each significant cluster.
  • New time-stamped event data from motion sensors and door contacts is accepted through the record data module of the estimate location module of the server.
  • the new time-stamped event data is live data as opposed to training/learning data and is recorded.
  • the new time-stamped event data is also forwarded to the create user location sequence module of the estimate location module of the server, which then creates a new user location sequence.
  • the create new user location sequence module then creates a new user location sequence.
  • the calculate distance module then calculates a distance between the medoids of each significant cluster and the new user location sequence.
  • the compare distance module accepts the greatest distances between the medoids of each cluster and each user location sequence of each cluster and then compares the distance between (the medoids of each significant cluster and the new user location sequence) and (the greatest distances between the medoids of each cluster and each user location sequence of each cluster). This is performed for each significant cluster.
  • the detect and report anomalies module then performs a test to determine if the new user location sequence is within the user's (normal) life routine. If the new user location sequence is not within the user's (normal) life routine then the new user location sequence represents anomalous behavior.
  • the distance between the medoids of each significant cluster and the new user location sequence is greater than the greatest distances between the medoids of each cluster and each user location sequence of each cluster, then there is anomalous behavior (the new user location sequence is not within the user's (normal) life routine).
  • Storage may include any form of RAM (DRAM, SRAM, etc.), ROM, (e.g., EPROM, EEPROM, etc.), hard disks, optical disks, flash memory, thumb drives, etc.
  • the communications interfaces may include wired line and/or wireless interfaces, for example to receive (accept) time-stamped event data and to provide alerts or other notifications to care givers about any anomalous behavior of the individual. It should be obvious that the server described above does not only handle (process) the data for a single individual but may process the data for a plurality of elderly or handicapped individuals.
  • detectability depends on the kind of anomalies that are to be detected. Indeed each time an anomaly to be detected is related to the fact that an individual is located in a room of his home or outside his home, it can be deemed that an estimation of the location of an individual has been carried out.
  • the proposed method and apparatus may be implemented in various forms of hardware, software, firmware, special purpose processors (e.g., math processors, computers or processors dedicated to machine learning), or a combination thereof.
  • Special purpose processors may include application specific integrated circuits (ASICs), reduced instruction set computers (RISCs) and/or field programmable gate arrays (FPGAs).
  • ASICs application specific integrated circuits
  • RISCs reduced instruction set computers
  • FPGAs field programmable gate arrays
  • the proposed method and apparatus is implemented as a combination of hardware and software.
  • the software is preferably implemented as an application program tangibly embodied on a program storage device. The application program may be uploaded to, and executed by, a machine comprising any suitable architecture.
  • the machine is implemented on a computer platform having hardware such as one or more central processing units (CPU), a random access memory (RAM), and input/output (I/O) interface(s).
  • the computer platform also includes an operating system and microinstruction code.
  • the various processes and functions described herein may either be part of the microinstruction code or part of the application program (or a combination thereof), which is executed via the operating system.
  • various other peripheral devices may be connected to the computer platform such as an additional data storage device and a printing device.
  • the elements shown in the figures may be implemented in various forms of hardware, software or combinations thereof. Preferably, these elements are implemented in a combination of hardware and software on one or more appropriately programmed general-purpose devices, which may include a processor, memory and input/output interfaces.
  • general-purpose devices which may include a processor, memory and input/output interfaces.
  • the phrase "coupled" is defined to mean directly connected to or indirectly connected with through one or more intermediate components. Such intermediate components may include both hardware and software based components.

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  • Psychology (AREA)
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EP15306725.1A 2015-10-29 2015-10-29 Détection d'une activité anormale pour personnes âgées et personnes handicapées Withdrawn EP3163545A1 (fr)

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CN110175554A (zh) * 2019-05-23 2019-08-27 南方科技大学 室内布局的检测方法、装置、设备及存储介质
CN110175554B (zh) * 2019-05-23 2024-01-12 南方科技大学 室内布局的检测方法、装置、设备及存储介质
CN111814436A (zh) * 2020-07-27 2020-10-23 上海观安信息技术股份有限公司 一种基于互信息和熵的用户行为序列检测方法及系统
CN111814436B (zh) * 2020-07-27 2023-10-17 上海观安信息技术股份有限公司 一种基于互信息和熵的用户行为序列检测方法及系统
CN112750276A (zh) * 2020-12-29 2021-05-04 杭州拓深科技有限公司 一种基于用电设备识别的独居老人看护方法及系统
CN112750276B (zh) * 2020-12-29 2023-07-04 杭州拓深科技有限公司 一种基于用电设备识别的独居老人看护方法及系统
CN114154034A (zh) * 2021-11-04 2022-03-08 杭州电魂网络科技股份有限公司 基于最长公共子序列的游戏工作室用户检测方法和系统

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