Background Sensor-based remote health monitoring can be used for the timely detection of health deterioration in people living with dementia with minimal impact on their day-to-day living. Anomaly detection approaches have been widely applied in various domains, including remote health monitoring. However, current approaches are challenged by noisy, multivariate data and low generalizability. Objective This study aims to develop an online, lightweight unsupervised learning–based approach to detect anomalies representing adverse health conditions using activity changes in people living with dementia. We demonstrated its effectiveness over state-of-the-art methods on a real-world data set of 9363 days collected from 15 participant households by the UK Dementia Research Institute between August 2019 and July 2021. Our approach was applied to household movement data to detect urinary tract infections (UTIs) and hospitalizations. Methods We propose and evaluate a solution based on Contextual Matrix Profile (CMP), an exact, ultrafast distance-based anomaly detection algorithm. Using daily aggregated household movement data collected via passive infrared sensors, we generated CMPs for location-wise sensor counts, duration, and change in hourly movement patterns for each patient. We computed a normalized anomaly score in 2 ways: by combining univariate CMPs and by developing a multidimensional CMP. The performance of our method was evaluated relative to Angle-Based Outlier Detection, Copula-Based Outlier Detection, and Lightweight Online Detector of Anomalies. We used the multidimensional CMP to discover and present the important features associated with adverse health conditions in people living with dementia. Results The multidimensional CMP yielded, on average, 84.3% recall with 32.1 alerts, or a 5.1% alert rate, offering the best balance of recall and relative precision compared with Copula-Based and Angle-Based Outlier Detection and Lightweight Online Detector of Anomalies when evaluated for UTI and hospitalization. Midnight to 6 AM bathroom activity was shown to be the most important cross-patient digital biomarker of anomalies indicative of UTI, contributing approximately 30% to the anomaly score. We also demonstrated how CMP-based anomaly scoring can be used for a cross-patient view of anomaly patterns. Conclusions To the best of our knowledge, this is the first real-world study to adapt the CMP to continuous anomaly detection in a health care scenario. The CMP inherits the speed, accuracy, and simplicity of the Matrix Profile, providing configurability, the ability to denoise and detect patterns, and explainability to clinical practitioners. We addressed the need for anomaly scoring in multivariate time series health care data by developing the multidimensional CMP. With high sensitivity, a low alert rate, better overall performance than state-of-the-art methods, and the ability to discover digital biomarkers of anomalies, the CMP is a clinically meaningful unsupervised anomaly detection technique extensible to multimodal data for dementia and other health care scenarios.

BACKGROUND Sensor-based remote health monitoring can be used for timely detection of health deterioration in people living with dementia with minimal impact on their day-to-day living. Anomaly detection approaches have been widely applied in various domains including remote health monitoring. However, current approaches are challenged by noisy and multivariate data and low generalizability. OBJECTIVE We aim to develop an online and lightweight unsupervised learning-based approach to detect anomalies representing adverse health conditions using activity changes in people living with dementia. We demonstrate the effectiveness of our method over state-of-the-art methods on a real-world dataset of 9363 days collected from 15 participant households, by the UK Dementia Research Institute between August 2019 and July 2021. Our approach is applied to household movement data to detect urinary tract infections (UTI) and hospitalization. METHODS We propose and evaluate a solution based on Contextual Matrix Profile (CMP), an exact and ultra-fast distance-based anomaly detection algorithm. Using daily aggregated household movement data collected via passive infrared (PIR) sensors, we generate CMPs for location-wise sensor counts, duration, and change in hourly movement patterns for each patient. We compute a single daily normalized anomaly score in two ways: by combining univariate CMPs, and by developing the multidimensional CMP. The performance of our anomaly detection method is evaluated relative to Angle-Based Outlier Detection (ABOD), Copula-Based Outlier Detection (COPOD) and Lightweight on-line detector of anomalies (LODA). We also use the multidimensional CMP to discover and present the important features associated with adverse health conditions in people living with dementia. RESULTS The multidimensional CMP yields 84.3% recall and offers the best balance of recall and relative precision compared to COPOD, LODA and ABOD when evaluated for urinary tract infections (UTI) and hospitalization. We validate early AM (midnight to 6 am) bathroom activity to be the most important cross-patient digital biomarker of anomalies indicative of UTI. We also demonstrate how CMP-based anomaly scoring can be used for a cross-patient view of anomaly patterns. CONCLUSIONS To the best of our knowledge, our work is the first real-world study to adapt the CMP to continuous anomaly detection in a healthcare scenario. The CMP inherits the speed, accuracy, exactness, and simplicity of the Matrix Profile, providing configurability, ability to denoise and detect patterns, and easy explainability to clinical practitioners. We address the need for anomaly scoring in multivariate time series healthcare data by developing the multidimensional CMP. With high sensitivity and a low alert rate, better overall performance than state-of-the-art methods, and the ability to discover digital biomarkers of anomalies, the CMP is a clinically meaningful unsupervised anomaly detection technique, extensible to multimodal data for dementia and other healthcare scenarios.

Background: Sensor-based remote health monitoring of persons living with dementia (PLwD) can be used to gain insights into their health and monitor the progression of their condition, with minimal intrusion. This helps minimize preventable hospital admissions, while allowing researchers to improve their understanding of dementia.Existing approaches for detecting activity and behavioural anomalies in PLwD are challenged by noise in data, lack of annotated datasets, multivariate data, scalability, data drift and explainability. Method: We propose and evaluate a solution based on the Matrix Profile, an exact, ultra-fast distance-based anomaly detection algorithm, specifically the Contextual Matrix Profile (CMP), to detect anomalies that may indicate unusual activity and onset of UTI. Daily household movement data collected via passive infrared (PIR) sensors are used to generate CMPs from location-wise sensor counts, duration and change in hourly movement patterns. We create CMP-based multivariate anomaly detection models to generate a single daily normalized anomaly score for each patient.We discover digital biomarkers of anomalies and evaluate our method vs. three state-of-the-art algorithms.Result: CMP-based models yield up to 85% recall with only a 5% alert rate, when evaluated on a subset of 9363 days from 15 participant households with 41 clinically validated incidences of urinary tract infections (UTI) and hospitalization, collected by the UK Dementia Research Institute between August 2019 and July 2021. Our multidimensional CMP model offers the best balance of recall vs. anomalies raised, with excellent generalisation. We discover that bathroom early AM activity (midnight to 6 am) is the prime cross-patient digital biomarker of anomalies. This validates findings in literature that unusual bathroom activity is a clinically significant feature in UTI for dementia. We also demonstrate a cross-patient view of anomaly patterns. Conclusion:We address the need for anomaly detection and scoring using multivariate time series sensor data in remote health monitoring. The CMP allows configurability, ability to denoise and detect patterns, and explainability to clinical practitioners. With higher sensitivity, fewer alerts and better overall performance than state-of-the-