Using Low-Resolution Non-Invasive Infrared Sensors to Classify Activities and Falls in Older Adults

Abstract

The population is aging worldwide, creating new challenges to the quality of life of older adults and their families. Falls are an increasing, but not inevitable, threat to older adults. Information technologies provide several solutions to address falls, but smart homes and the most available solutions require expensive and invasive infrastructures. In this study, we propose a novel approach to classify and detect falls of older adults in their homes through low-resolution infrared sensors that are affordable, non-intrusive, do not disturb privacy, and are more acceptable to older adults. Using data collected between 2019 and 2020 with the eHomeseniors platform, we determine activity scores of older adults moving across two rooms in a house and represent an older adult fall through skeletonization. We find that our twofold approach effectively detects activity patterns and precisely identifies falls. Our study provides insights to physicians about the daily activities of their older adults and could potentially help them make decisions in case of abnormal behavior.

Improvement in Quality of Life with Use of Ambient-Assisted Living: Clinical Trial with Older Persons in the Chilean Population

“…The developed product, Quida, is the result of a process of continuous improvement through various research studies [ 29 , 30 , 31 , 32 , 33 , 34 , 35 , 36 ]. In particular, [ 29 ] used very low-resolution thermal sensors to classify falls and then alert care personnel.…”

“…However, unintentional falls in older adults are a high-risk area, and their incidence imposes a significant economic burden on the health system [ 27 ]. Therefore, portable inertial sensors have grown in popularity as a means to objectively assess the risk of falls [ 28 ], generating studies aimed at its validation as non-intrusive systems for controlled environments to detect falls and other events and that do not compromise the privacy of users [ 29 , 30 , 31 , 32 , 33 , 34 , 35 , 36 ], being also relevant to obtain evidence of the impact on the quality of life of this age group.…”

Evaluation of Machine Learning Techniques for Classifying and Balancing Data on an Unbalanced Mini-Mental State Examination Test Data Collection Applied in Chile

“…In our study, the results form 103 participants were added to the dataset based on inclusion and exclusion criteria (see Table 1). We defined these inclusion and exclusion criteria based on our previous experience [11] [12] performing an EQ5D [13] survey in research projects with older adults. As we have gained experience dealing with older adults, the inclusion and exclusion criteria defined in Table 1 have helped us obtain a meaningful set of older adults to conduct our study.…”

Barriers and Facilitators of Ambient Assisted Living Systems: A Systematic Literature Review

“…To mitigate this threat, we invited health professionals and technology experts who collaborated with our study to discuss and analyze each primary study in order to obtain feedback. Additionally, we used our previous experience [ 26 ] in AALS to identify potential gains and pains.…”