Research Project
9622314
ABSTRACT With a Phase I/II STTR grant award from NIA, BioSensics in collaboration with Baylor College of Medicine (BCM) successfully developed and commercialized an advanced physical activity monitoring system (PAMSys?) for older adults. PAMSys enables continuous remote monitoring of physical activity, fall risk, and fall incidents. The fall detection technology of PAMSys has become the gold standard in the medical alert industry through multiple licensing agreements. To date, more than 100,000 medical alert devices with BioSensics technology have been sold by our licensing partners. However, PAMSys is not capable of monitoring Instrumental Activities of Daily Living (IADL) such as cooking, shopping, and managing medication. Monitoring IADL is essential for timely diagnosis of dementia, monitoring disease progression, and determining when additional care services are needed. In one of our pilot clinical studies using PAMSys, we identified specific activity patterns (e.g., transitions between different postures, short walking bouts) in individuals with cognitive impairment who were monitored during activities of daily living. In addition, BioSensics has developed an initial prototype of a smart wireless device, called CliQ, that enables interactions with objects at home, while also simplifying the user interaction with smart home devices and smartphone apps. Armed with this technical expertise and pilot clinical data, we propose to design and commercialize a platform for continuous monitoring of IADL. This innovative solution, called IADLSys?, is based on the following three technologies that will work collectively to provide crucial insight into deterioration in cognitive status and trajectory toward loss of independence: 1) wireless tags that are attached to various objects of interest in the user?s living environment, 2) a wearable sensor that measures physical activity, as well as proximity to the wireless tags, and 3) software that unobtrusively monitors the usage of applications associated with daily functioning and social engagement to provide a picture of the user?s virtual IADL to complement the monitoring of their physical IADL (usage time only, no private application data is monitored). IADLSys also includes a hub to transfer all data to a secure backend server that will be used for data storage and processing. In Phase I, we will design an initial version of IADLSys and examine the feasibility of the proposed platform for identifying IADL of interest, including using a telephone, preparing a meal, doing laundry, taking medication, vacuuming, and others in the target population. In Phase II, we will complete the development of IADLSys including implementation of live uploading protocols and the cloud-computing backend to host collected data. A robust data-security architecture will be implemented to protect patient?s privacy. In addition, we will carry out a clinical study to evaluate if the data gathered and analyzed by IADLSys can discriminate those with Mild Neurocognitive Disorder (previously termed Mild Cognitive Impairment), and Major Neurocognitive Disorder due to Alzheimer?s Disease (AD) with mild severity, from cognitively intact aged matched healthy individuals.
