Department of Computer Science, Kaliachak College, University of Gour Banga, Malda 732101, India.
Department of Computer & System Sciences, Visva-Bharati University, Bolpur 731235, India.
Sensors (Basel). 2022 Nov 23;22(23):9067. doi: 10.3390/s22239067.
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.
由于许多因素，包括通过更好的医疗保健延长寿命、饮食变化、体育活动等，全球人口正在老龄化。我们也在见证各种频繁的流行病和大流行。现有的医疗保健系统未能在这些频繁爆发期间为我们的老年人（老年人）提供所需的护理和支持。基于复杂传感器的家庭护理系统可能是解决这一全球危机的有效方法。监测系统是任何家庭护理系统的关键组成部分。有证据表明，通过不同的视觉和音频传感器以非侵入性的方式实施时，它们更有用。人工智能 (AI) 和计算机视觉 (CV) 技术可能非常适合此目的。由于基于 RGB 图像的 CV 技术可能会侵犯隐私，人们通常不愿使用使用这项技术的家庭护理系统。基于深度、热和音频的 CV 技术可能是有意义的替代品。由于需要监测更大的区域，本文系统地讨论了使用深度传感器作为主要数据采集技术的最新技术。我们主要关注跌倒检测和其他与健康相关的身体模式。由于步态参数可能有助于检测这些活动，我们还分别考虑了基于深度传感器的步态参数。本文还讨论了与术语、综述、流行数据集调查以及未来范围相关的主题。
