Verification of Daily Activities of Older Adults: A Simple, Non-Intrusive, Low-Cost Approach

International audienceThis paper presents an approach to verifying the activities of daily living of older adults at their home. We verify activities, instead of inferring them, because our monitoring approach is driven by routines, initially sketched by users in their environment. Monitoring is supported by a lightweight sensor infrastructure, comprising non-intrusive, low-cost, wireless devices. Verification is performed by applying a simple formula to sensor log data, for each activity of interest. The result value determines whether an activity has been performed.We have conducted an experimental study to validate our approach. To do so, four participants have been monitored during five days at their home, equipped with sensors. When applied to the log data, our formulas were able to automatically verify that a list of activities were performed. They produced the same interpretations, using Signal Detection Theory, as a third party, manually analyzing the log data

What Is Happening Now? - Detection of Activities of Daily Living from Simple Visual Features

We propose and investigate a paradigm for activity recognition, distinguishing the "on-going activity" recognition task (OGA) from that addressing "complete activities" (CA). The former starts from a time interval and aims to discover which activities are going on inside it. The latter, in turn, focuses on terminated activities and amounts to taking an external perspective on activities. We argue that this distinction is quite natural and the OGA task has a number of interesting properties; e.g., the possibility of reconstructing complete activities in terms of on-going ones, the avoidance of the thorny issue of activity segmentation, and a straightforward accommodation of complex activities, etc. Moreover, some plausible properties of the OGA task are discussed and then investigated in a classification study, addressing: the dependence of classification performance on the duration of time windows and its relationship with actional types (homogeneous vs. non-homogeneous activities), and on the assortments of features used. Three types of visual features are exploited, obtained from a data set that tries to balance the pros and cons of laboratory-based and naturalistic ones. The results provide partial confirmation to the hypothesis and point to relevant open issues for future work

LPcomS: Towards a Low Power Wireless Smart-Shoe System for Gait Analysis in People with Disabilities

Gait analysis using smart sensor technology is an important medical diagnostic process and has many applications in rehabilitation, therapy and exercise training. In this thesis, we present a low power wireless smart-shoe system (LPcomS) to analyze different functional postures and characteristics of gait while walking. We have designed and implemented a smart-shoe with a Bluetooth communication module to unobtrusively collect data using smartphone in any environment. With the design of a shoe insole equipped with four pressure sensors, the foot pressure is been collected, and those data are used to obtain accurate gait pattern of a patient. With our proposed portable sensing system and effective low power communication algorithm, the smart-shoe system enables detailed gait analysis. Experimentation and verification is conducted on multiple subjects with different gait including free gait. The sensor outputs, with gait analysis acquired from the experiment, are presented in this thesis