Development of a Sensor-Based Behavioral Monitoring Solution to Support Dementia Care

Abstract

Background Mobile and wearable technology presents exciting opportunities for monitoring behavior using widely available sensor data. This could support clinical research and practice aimed at improving quality of life among the growing number of people with dementia. However, it requires suitable tools for measuring behavior in a natural real-life setting that can be easily implemented by others. Objective The objectives of this study were to develop and test a set of algorithms for measuring mobility and activity and to describe a technical setup for collecting the sensor data that these algorithms require using off-the-shelf devices. Methods A mobility measurement module was developed to extract travel trajectories and home location from raw GPS (global positioning system) data and to use this information to calculate a set of spatial, temporal, and count-based mobility metrics. Activity measurement comprises activity bout extraction from recognized activity data and daily step counts. Location, activity, and step count data were collected using smartwatches and mobile phones, relying on open-source resources as far as possible for accessing data from device sensors. The behavioral monitoring solution was evaluated among 5 healthy subjects who simultaneously logged their movements for 1 week. Results The evaluation showed that the behavioral monitoring solution successfully measures travel trajectories and mobility metrics from location data and extracts multimodal activity bouts during travel between locations. While step count could be used to indicate overall daily activity level, a concern was raised regarding device validity for step count measurement, which was substantially higher from the smartwatches than the mobile phones. Conclusions This study contributes to clinical research and practice by providing a comprehensive behavioral monitoring solution for use in a real-life setting that can be replicated for a range of applications where knowledge about individual mobility and activity is relevant.

Adapting Mobile and Wearable Technology to Provide Support and Monitoring in Rehabilitation for Dementia: Feasibility Case Series

“…This transition is evident in Table 4, which shows a marked increase in the total time spent cycling per week from the point at which he first reported cycling (week 5). The cycling time prior to week 5 was quite possibly the result of misclassifications in the activity recognition from Google, since confusion between vehicle and bicycle activities is a known issue [17].…”

“…paired with a smartwatch (ie, Sony SmartWatch 3 running Android Wear), a mobile self-reporting module, and an app for secure collection of sensor data and logs from the devices (see Figure 2). A detailed description of the system is available in Thorpe et al [17].…”

“…Location and activity data, including steps and recognized activities, were recorded and used to calculate a set of mobility and activity metrics. The set of algorithms used to measure mobility and activity behavior was described in detail in an earlier study by Thorpe et al [17]; this set combined a range of metrics used for similar monitoring purposes in related literature [21-24].…”

Designing P4 healthcare interventions for managing cognitive decline and dementia: where are we at?

“…A second reason could be alternate priorities of clinical researchers and emphasis lying elsewhere than proposing tools and methods for the investigation of system properties, an activity usually the forte of engineering designers. Some early attempts to systematise the considerations of data-centric interventions for managing cognitive impairment, for instance, are presented in Thorpe and Forchhammer (2016) and Thorpe, Forchhammer, and Maier (2019). The challenge of developing tools and methods for P4 or any other new conceptualisation of healthcare delivery also comes from the fact that the ontology of the concepts advocated in the clinical literature is often not consensual or is lacking precise definitions.…”

Sustaining Behaviour Change Through Immersive Technologies: Trends, Perspectives, and Approaches

“…The last decades have seen a remarkable increase in technology-enabled interventions reflecting the advances in communication and information technologies (Janevic and Connell, 2019). These include deliveries of interventions via mobile devices (mHealth), apps, via the Internet (eHealth) or wearable systems (Naslund et al, 2017;Thorpe et al, 2016Thorpe et al, , 2019. The design of technology-enabled interventions often makes use of digital components incorporating elements such as apps, GPS, cameras, accelerometers, and further sensor information (Janevic and Connell, 2019).…”