Behavior Analysis through Multimodal Sensing for Care of Parkinson’s and Alzheimer’s Patients

Abstract

The analysis of multimodal data collected by innovative imaging sensors, Internet of Things devices, and user interactions can provide smart and automatic distant monitoring of Parkinson's and Alzheimer's patients and reveal valuable insights for early detection and/or prevention of events related to their health. This article describes a novel system that involves data capturing and multimodal fusion to extract relevant features, analyze data, and provide useful recommendations. The system gathers signals from diverse sources in health monitoring environments, understands the user behavior and context, and triggers proper actions for improving the patient's quality of life. The system offers a multimodal, multi-patient, versatile approach not present in current developments. It also offers comparable or improved results for detection of abnormal behavior in daily motion. The system was implemented and tested during 10 weeks in real environments involving 18 patients.

Sensing Technology to Monitor Behavioral and Psychological Symptoms and to Assess Treatment Response in People With Dementia. A Systematic Review

“…Firstly, almost half of studies across the three categories represent ways in which temporarily dense data on motion can be processed and aggregated as proxy for behavior. Findings from these studies indicate high validity of using motion data to detect and track behavioral symptoms such as sleep disturbances, agitation, and wandering (Rowe et al, 2009;Bankole et al, 2012;Sacco et al, 2012;Yamakawa et al, 2012;Aloulou et al, 2013;Galambos et al, 2013;Stucki et al, 2014;Fleiner et al, 2016;Hattink et al, 2016;Jekel et al, 2016;Lazarou et al, 2016;Merilahti et al, 2016;Alvarez et al, 2018;Enshaeifar et al, 2018). Continuous motion monitoring of people with dementia using sensor technology provides informal caregivers and health care providers with the ability to more immediately and accurately diagnose and manage behavioral disturbances and can help to delay admission to long-term care or inpatient facilities.…”

An FPGA Based Tracking Implementation for Parkinson’s Patients

“…The overall system has been developed following a user-centered methodology and validated in real life scenarios, such us the one shown in Figure 1. The platform, described in [15], consists of different sensors: visual, motion and depth, that together form a multi-sensor tracking system [16]. The types of devices vary based on the requirements analyzed for the different scenarios and the typology of patients, including:…”

A Partial Least-Squares Regression Model to Measure Parkinson’s Disease Motor States Using Smartphone Data

“…Multimodal analysis includes data from different sensors with the aim of finding the relationships between multimodal data and different groups of patients in order to identify patient trajectories as well as provide means for optimal tailoring of treatments based on individual patient profiles [3]. There are several studies reporting results on objective measures in PD using multimodal data including combination of speech, handwriting, and gait [4], voice, posture, gait, finger tapping, and response time [5], leg agility, sit-to-stand, and gait tasks [6], and daily motion patterns, freezing of gait, among others [7]. Combining data from multiple modalities or even extracting multiple variables from a single modality poses challenges during development of data-driven models for measuring the severity of the symptoms.…”