An Instrumented Apartment to Monitor Human Behavior: A Pilot Case Study in the NeuroTec Loft

Gerber, Stephan M.; Single, Michael; Knobel, Samuel E. J.; Schütz, Narayan; Bruhin, Lena C; Botros, Angela; Naef, Aileen C.; Schindler, Kaspar; Nef, Tobias

doi:10.3390/s22041657

Cited by 3 publications

(3 citation statements)

References 22 publications

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“…In a newly established research facility, further studies regarding cognitive abilities, activities of daily living and home-based health assessments will continue [ 46 ].…”

Section: Discussionmentioning

confidence: 99%

Eigenbehaviour as an Indicator of Cognitive Abilities

Botros

Schütz

Röcke

et al. 2022

Sensors

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With growing use of machine learning algorithms and big data in health applications, digital measures, such as digital biomarkers, have become highly relevant in digital health. In this paper, we focus on one important use case, the long-term continuous monitoring of cognitive ability in older adults. Cognitive ability is a factor both for long-term monitoring of people living alone as well as a relevant outcome in clinical studies. In this work, we propose a new potential digital biomarker for cognitive abilities based on location eigenbehaviour obtained from contactless ambient sensors. Indoor location information obtained from passive infrared sensors is used to build a location matrix covering several weeks of measurement. Based on the eigenvectors of this matrix, the reconstruction error is calculated for various numbers of used eigenvectors. The reconstruction error in turn is used to predict cognitive ability scores collected at baseline, using linear regression. Additionally, classification of normal versus pathological cognition level is performed using a support-vector machine. Prediction performance is strong for high levels of cognitive ability but grows weaker for low levels of cognitive ability. Classification into normal and older adults with mild cognitive impairment, using age and the reconstruction error, shows high discriminative performance with an ROC AUC of 0.94. This is an improvement of 0.08 as compared with a classification with age only. Due to the unobtrusive method of measurement, this potential digital biomarker of cognitive ability can be obtained entirely unobtrusively—it does not impose any patient burden. In conclusion, the usage of the reconstruction error is a strong potential digital biomarker for binary classification and, to a lesser extent, for more detailed prediction of inter-individual differences in cognition.

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“…In a newly established research facility, further studies regarding cognitive abilities, activities of daily living and home-based health assessments will continue [ 46 ].…”

Section: Discussionmentioning

confidence: 99%

Eigenbehaviour as an Indicator of Cognitive Abilities

Botros

Schütz

Röcke

et al. 2022

Sensors

Self Cite

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“…Notably, the results of both studies, study 1 and study 2, have yet not been published. Lastly, instance 4 was used in a pilot case study, hereafter referred to as study 3 (n=4 for 30 days), in which, Gerber et al [ 65 ] measured ADL during a 12-hour overnight stay in an instrumented apartment. The purpose of this use case study was to demonstrate that the proposed system can be used to continuously assess digital biomarkers (eg, gait parameters and ADL) during a prolonged period and therefore represents a reliable measuring method for conducting future clinical studies in the apartment (eg, measure the change in motor functions in patients with Parkinson disease or multiple sclerosis).…”

Section: Methodsmentioning

confidence: 99%

“…The sensors used to generate the data sets ( Table 2 ) were Doppler radars, seismographs, LIDARs, magnetic door sensors, a motion tracking camera system, a pressure mattress for tracking activity in bed, an infrared camera, night and day cameras, passive infrared sensors, a microphone system, devices for measuring power consumption, and environmental sensors (eg, temperature, humidity, and water flow sensors). These sensors have been shown to be particularly useful in biomedical research concerning the analysis of motor and nonmotor functions [ 65 , 66 ] and were thus integrated into the SRS system. The performance of the SRS was statistically evaluated based on the data collected through deployed instances used in the studies.…”

Section: Methodsmentioning

confidence: 99%

Development of an Open-source and Lightweight Sensor Recording Software System for Conducting Biomedical Research: Technical Report

Single¹,

Bruhin²,

Schütz³

et al. 2023

JMIR Form Res

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Background Digital sensing devices have become an increasingly important component of modern biomedical research, as they help provide objective insights into individuals’ everyday behavior in terms of changes in motor and nonmotor symptoms. However, there are significant barriers to the adoption of sensor-enhanced biomedical solutions in terms of both technical expertise and associated costs. The currently available solutions neither allow easy integration of custom sensing devices nor offer a practicable methodology in cases of limited resources. This has become particularly relevant, given the need for real-time sensor data that could help lower health care costs by reducing the frequency of clinical assessments performed by specialists and improve access to health assessments (eg, for people living in remote areas or older adults living at home). Objective The objective of this paper is to detail the end-to-end development of a novel sensor recording software system that supports the integration of heterogeneous sensor technologies, runs as an on-demand service on consumer-grade hardware to build sensor systems, and can be easily used to reliably record longitudinal sensor measurements in research settings. Methods The proposed software system is based on a server-client architecture, consisting of multiple self-contained microservices that communicated with each other (eg, the web server transfers data to a database instance) and were implemented as Docker containers. The design of the software is based on state-of-the-art open-source technologies (eg, Node.js or MongoDB), which fulfill nonfunctional requirements and reduce associated costs. A series of programs to facilitate the use of the software were documented. To demonstrate performance, the software was tested in 3 studies (2 gait studies and 1 behavioral study assessing activities of daily living) that ran between 2 and 225 days, with a total of 114 participants. We used descriptive statistics to evaluate longitudinal measurements for reliability, error rates, throughput rates, latency, and usability (with the System Usability Scale [SUS] and the Post-Study System Usability Questionnaire [PSSUQ]). Results Three qualitative features (event annotation program, sample delay analysis program, and monitoring dashboard) were elaborated and realized as integrated programs. Our quantitative findings demonstrate that the system operates reliably on consumer-grade hardware, even across multiple months (>420 days), providing high throughput (2000 requests per second) with a low latency and error rate (<0.002%). In addition, the results of the usability tests indicate that the system is effective, efficient, and satisfactory to use (mean usability ratings for the SUS and PSSUQ were 89.5 and 1.62, respectively). Conclusions Overall, this sensor recording software could be leveraged to test sensor devices, as well as to develop and validate algorithms that are able to extract digital measures (eg, gait parameters or actigraphy). The proposed software could help significantly reduce barriers related to sensor-enhanced biomedical research and allow researchers to focus on the research questions at hand rather than on developing recording technologies.

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Development of an Open-source and Lightweight Sensor Recording Software System for Conducting Biomedical Research: Technical Report (Preprint)

Single¹,

Bruhin²,

Schütz³

et al. 2022

Preprint

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BACKGROUND Digital sensing devices have become an increasingly important component of modern biomedical research, as they help provide objective insights into individuals’ everyday behavior in terms of changes in motor and nonmotor symptoms. However, there are significant barriers to the adoption of sensor-enhanced biomedical solutions in terms of both technical expertise and associated costs. The currently available solutions neither allow easy integration of custom sensing devices nor offer a practicable methodology in cases of limited resources. This has become particularly relevant, given the need for real-time sensor data that could help lower health care costs by reducing the frequency of clinical assessments performed by specialists and improve access to health assessments (eg, for people living in remote areas or older adults living at home). OBJECTIVE The objective of this paper is to detail the end-to-end development of a novel sensor recording software system that supports the integration of heterogeneous sensor technologies, runs as an on-demand service on consumer-grade hardware to build sensor systems, and can be easily used to reliably record longitudinal sensor measurements in research settings. METHODS The proposed software system is based on a server-client architecture, consisting of multiple self-contained microservices that communicated with each other (eg, the web server transfers data to a database instance) and were implemented as Docker containers. The design of the software is based on state-of-the-art open-source technologies (eg, Node.js or MongoDB), which fulfill nonfunctional requirements and reduce associated costs. A series of programs to facilitate the use of the software were documented. To demonstrate performance, the software was tested in 3 studies (2 gait studies and 1 behavioral study assessing activities of daily living) that ran between 2 and 225 days, with a total of 114 participants. We used descriptive statistics to evaluate longitudinal measurements for reliability, error rates, throughput rates, latency, and usability (with the System Usability Scale [SUS] and the Post-Study System Usability Questionnaire [PSSUQ]). RESULTS Three qualitative features (event annotation program, sample delay analysis program, and monitoring dashboard) were elaborated and realized as integrated programs. Our quantitative findings demonstrate that the system operates reliably on consumer-grade hardware, even across multiple months (>420 days), providing high throughput (2000 requests per second) with a low latency and error rate (<0.002%). In addition, the results of the usability tests indicate that the system is effective, efficient, and satisfactory to use (mean usability ratings for the SUS and PSSUQ were 89.5 and 1.62, respectively). CONCLUSIONS Overall, this sensor recording software could be leveraged to test sensor devices, as well as to develop and validate algorithms that are able to extract digital measures (eg, gait parameters or actigraphy). The proposed software could help significantly reduce barriers related to sensor-enhanced biomedical research and allow researchers to focus on the research questions at hand rather than on developing recording technologies. CLINICALTRIAL

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An Instrumented Apartment to Monitor Human Behavior: A Pilot Case Study in the NeuroTec Loft

Cited by 3 publications

References 22 publications

Eigenbehaviour as an Indicator of Cognitive Abilities

Eigenbehaviour as an Indicator of Cognitive Abilities

Development of an Open-source and Lightweight Sensor Recording Software System for Conducting Biomedical Research: Technical Report

Development of an Open-source and Lightweight Sensor Recording Software System for Conducting Biomedical Research: Technical Report (Preprint)

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