Detecting deviations from activities of daily living routines using kinect depth maps and power consumption data

Pazhoumand-Dar, Hossein; Armstrong, Leisa; Tripathy, Amiya Kumar

doi:10.1007/s12652-019-01447-3

Cited by 9 publications

(7 citation statements)

References 27 publications

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“…The four networks have been applied on two public data sets, namely SIMADL Dataset [ 34 ] and MobiAct Dataset [ 35 ], and the experimental results demonstrate that hybridization of CNN with LSTM provides the best results in the case of detecting temporal and spatial abnormal behavior. In [ 36 ] an unsupervised approach for learning the ADL routine of elders living alone and detecting deviations from it is proposed. The daily living activities are identified by correlating the elder’s location in the house with the location’s power consumption.…”

Section: Related Workmentioning

confidence: 99%

Identifying and Monitoring the Daily Routine of Seniors Living at Home

Chifu

Pop

Demjen

et al. 2022

Sensors

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As the population in the Western world is rapidly aging, the remote monitoring solutions integrated into the living environment of seniors have the potential to reduce the care burden helping them to self-manage problems associated with old age. The daily routine is considered a useful tool for addressing age-related problems having additional benefits for seniors like reduced stress and anxiety, increased feeling of safety and security. In this paper, we propose a solution for identifying the daily routines of seniors using the monitored activities of daily living and for inferring deviations from the routines that may require caregivers’ interventions. A Markov model-based method is defined to identify the daily routines, while entropy rate and cosine functions are used to measure and assess the similarity between the daily monitored activities in a day and the inferred routine. A distributed monitoring system was developed that uses Beacons and trilateration techniques for monitoring the activities of older adults. The results are promising, the proposed techniques can identify the daily routines with confidence concerning the activity duration of 0.98 and the sequence of activities in the interval of [0.0794, 0.0829]. Regarding deviation identification, our method obtains 0.88 as the best sensitivity value with an average precision of 0.95.

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Section: Related Workmentioning

confidence: 99%

Identifying and Monitoring the Daily Routine of Seniors Living at Home

Chifu

Pop

Demjen

et al. 2022

Sensors

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“…Identifying a person's daily routine is an important issue because it allows us to detect changes that may occur in a person's behavior, changes can be related to health problems. The literature approaches can be grouped into three main categories, namely, unsupervised approaches [14,[16][17][18][19][20], supervised approaches [21][22][23][24][25], and statistical or model-based approaches [15][16][17][18]. All these state-of-the-art approaches define the daily routine of a person as a sequence of activities performed throughout the day.…”

Section: Related Workmentioning

confidence: 99%

“…Several state-of-the-art studies [14,[16][17][18] are addressing the detection of older adults' daily routines. However, most approaches use small sequences of activities or even one type of activity and do not consider the entire day.…”

Section: Introductionmentioning

confidence: 99%

Deep Learning, Mining, and Collaborative Clustering to Identify Flexible Daily Activities Patterns

Chifu

Pop

Rancea

et al. 2022

Sensors

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The monitoring of the daily life activities routine is beneficial, especially in old age. It can provide relevant information on the person’s health state and wellbeing and can help identify deviations that signal care deterioration or incidents that require intervention. Existing approaches consider the daily routine as a rather strict sequence of activities which is not usually the case. In this paper, we propose a solution to identify flexible daily routines of older adults considering variations related to the order of activities and activities timespan. It combines the Gap-BIDE algorithm with a collaborative clustering technique. The Gap-BIDE algorithm is used to identify the most common patterns of behavior considering the elements of variations in activities sequence and the period of the day (i.e., night, morning, afternoon, and evening) for increased pattern mining flexibility. K-means and Hierarchical Clustering Agglomerative algorithms are collaboratively used to address the time-related elements of variability in daily routine like activities timespan vectors. A prototype was developed to monitor and detect the daily living activities based on smartwatch data using a deep learning architecture and the InceptionTime model, for which the highest accuracy was obtained. The results obtained are showing that the proposed solution can successfully identify the routines considering the aspects of flexibility such as activity sequences, optional and compulsory activities, timespan, and start and end time. The best results were obtained for the collaborative clustering solution that considers flexibility aspects in routine identification, providing coverage of monitored data of 89.63%.

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“…This system applies to older adults monitoring. A Kinect depth sensor is used in [37] for identifying deviating activities that could constitute an abnormality in a smart home environment. A data-driven technique is used to define fuzzy sets over attributes of the occupant's behaviour and a fuzzy inference engine with a membership function is used to identify an abnormal pattern.…”

Section: Related Workmentioning

confidence: 99%

“…a is NOT an outlier −1 a is an outlier [16] Inferred ADL data CAAB and regression model Correlation and RMSE [22] Inferred ADL data Causal association rule and Markov logic network Accuracy, precision and recall [49] Inferred ADL data OC-SVM Accuracy [2,5] Inferred ADL data IADL-C, Regression and classification model RMSE, correlation, F-Score, accuracy, sensitivity and ROC-AUC [7] Raw binary data CNN + LSTM Sensitivity, specificity [30] Raw binary and inferred ADL data ESN RMSE [24] Inferred ADL data DBSCAN Precision and recall [27] Inferred ADL data OC-SVM Error rate, F-measure and accuracy [6] Raw binary data Vanilla RNN, GRU, LSTM Accuracy [18] Inferred ADL data DBSCAN EER [20] Inferred ADL data HMM + FRBS Accuracy, precision, recall, F-measure, ROC-AUC [50] Inferred ADL and synthetic data Ensemble (OC-SVM, LOF, iForest, RCE) Accuracy [34,35] Raw binary data SOM Accuracy [25] Raw binary data Entropy measures Accuracy, precision and recall [47] Inferred ADL data HCRF & SVM Accuracy [4] ADL data (video stream) 3D-CNN + LSTM Accuracy, precision, recall, F-measre and specificity [37] ADL data (depth) Fuzzy inference system Percentage error rate [32] Inferred ADL data (ODHMAD) Precision, false alarm and false prediction rate [51] Inferred ADL data LSTM, CNN, CNN-LSTM and autoencoder Accuracy…”

Section: The Radial Basis Function (Rbf) Kernel With Spread Parametermentioning

confidence: 99%

Detecting Anomaly and Its Sources in Activities of Daily Living

2021

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To support the independent living and improve the quality of life for the increasing ageing population, system for monitoring their daily routine and detecting anomalies in the routine is required. Existing anomaly detection systems are unable to identify the sources of the abnormalities, thereby hindering the development of adaptive monitoring systems with reduced false prediction rate. In this paper, an approach for identifying the sources of abnormalities in human activities of daily living is proposed. Anomalies are detected by modelling the existing activity data representing the usual behavioural routine of an individual to serve as a baseline model. Subsequent activities deviating from the baseline are then classified as outliers or anomalies. An ensemble of one-class support vector machine, isolation forest, robust covariance estimator and local outlier factor is utilised for the anomaly detection achieving an accuracy of $$98\%$$ 98 % . The proposed approach for identifying anomaly sources is based on the concept of similarity measure using distance functions. Two methods for measuring the pairwise distance of the features of the activity data termed as one vs one similarity measure and one vs all similarity measure are proposed. Experimental evaluation of the proposed approach on activities of daily living datasets has shown the credibility of the proposed approach for utilisation in an in-home monitoring system.

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Detecting deviations from activities of daily living routines using kinect depth maps and power consumption data

Cited by 9 publications

References 27 publications

Identifying and Monitoring the Daily Routine of Seniors Living at Home

Identifying and Monitoring the Daily Routine of Seniors Living at Home

Deep Learning, Mining, and Collaborative Clustering to Identify Flexible Daily Activities Patterns

Detecting Anomaly and Its Sources in Activities of Daily Living

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