Articolo in rivista, 2022, ENG, 10.3390/s22239067

In-Home Older Adults' Activity Pattern Monitoring Using Depth Sensors: A Review

Momin, Md Sarfaraz; Sufian, Abu; Barman, Debaditya; Dutta, Paramartha; Dong, Mianxiong; Leo, Marco

University of Gour Banga; National Reseach Council of Italy (CNR), Institute of Applied Sciences and Intelligent Systems "Eduardo Caianiello"; Visva-Bharati University; Muroran Institute of Technology

The global population is aging due to many factors, including longer life expectancy through better healthcare, changing diet, physical activity, etc. We are also witnessing various frequent epidemics as well as pandemics. The existing healthcare system has failed to deliver the care and support needed to our older adults (seniors) during these frequent outbreaks. Sophisticated sensor-based in-home care systems may offer an effective solution to this global crisis. The monitoring system is the key component of any in-home care system. The evidence indicates that they are more useful when implemented in a non-intrusive manner through different visual and audio sensors. Artificial Intelligence (AI) and Computer Vision (CV) techniques may be ideal for this purpose. Since the RGB imagery-based CV technique may compromise privacy, people often hesitate to utilize in-home care systems which use this technology. Depth, thermal, and audio-based CV techniques could be meaningful substitutes here. Due to the need to monitor larger areas, this review article presents a systematic discussion on the state-of-the-art using depth sensors as primary data-capturing techniques. We mainly focused on fall detection and other health-related physical patterns. As gait parameters may help to detect these activities, we also considered depth sensor-based gait parameters separately. The article provides discussions on the topic in relation to the terminology, reviews, a survey of popular datasets, and future scopes.

Sensors (Basel) 22 (23)

Keywords

classification of sensor data, computer vision, depth imagery, fall detection, gait analysis, HAR, smart home, survey

CNR authors

Leo Marco

CNR institutes

ID: 475805

Year: 2022

Type: Articolo in rivista

Creation: 2022-12-31 10:57:03.000

Last update: 2022-12-31 10:57:03.000

CNR authors

External IDs

CNR OAI-PMH: oai:it.cnr:prodotti:475805

DOI: 10.3390/s22239067

Scopus: 2-s2.0-85143835315