A Review of Wearable Technologies for Elderly Care that Can Accurately Track Indoor Position, Recognize Physical Activities and Monitor Vital Signs in Real Time

Abstract

Rapid growth of the aged population has caused an immense increase in the demand for healthcare services. Generally, the elderly are more prone to health problems compared to other age groups. With effective monitoring and alarm systems, the adverse effects of unpredictable events such as sudden illnesses, falls, and so on can be ameliorated to some extent. Recently, advances in wearable and sensor technologies have improved the prospects of these service systems for assisting elderly people. In this article, we review state-of-the-art wearable technologies that can be used for elderly care. These technologies are categorized into three types: indoor positioning, activity recognition and real time vital sign monitoring. Positioning is the process of accurate localization and is particularly important for elderly people so that they can be found in a timely manner. Activity recognition not only helps ensure that sudden events (e.g., falls) will raise alarms but also functions as a feasible way to guide people's activities so that they avoid dangerous behaviors. Since most elderly people suffer from age-related problems, some vital signs that can be monitored comfortably and continuously via existing techniques are also summarized. Finally, we discussed a series of considerations and future trends with regard to the construction of "smart clothing" system.

Indoor positioning system for movement path analysis in healthcare institutions

“…In general, tracking methods have been used with people who suffer from cognitive impairment, such as dementia [6]. These devices may reduce the time and effort required to find a missing person, increase the independence, autonomy and freedom of a person with middle-to moderate dementia, and reduce the caregivers level of stress [12].…”

“…[1][2][3]) or smart-watches [4], and it may also be implemented in garments (see e.g. [5,6]). The cost and effort needed to obtain and deploy a fancy system may however become arduous for research purposes.…”

Combination of Smartphone MEMS Sensors and Environmental Prior Information for Pedestrian Indoor Positioning

“…In the paper [30], the author used the deep learning method to process the pictures taken by the smartphone camera, then identifies the user's location, and uses the particle filter algorithm constrained by the scene information to determine the final location. In reference [31], the author used machine learning method to analyze the self-contained MEMS sensor data in intelligent wearable devices, and then identified the activity characteristics of the elderly in the indoor environment, and finally constructed a set of intelligent home service system. In reference [32], the author used a stack Auto-encoder network to identify the characteristics of Internet of things data in an indoor environment, and to build a fingerprint database for online location.…”

Wearable accelerometer based extended sleep position recognition

“…In this study 3-axis accelerometer data was collected using three Shimmer3 devices, worn on the ankles and the chest in both a simulated and real world setting. To improve the usability and comfort of wearable solutions there is a need for this type of sensor to be integrated into clothing [22]. Some integrated sensors for motion detection are already on the market like Enflux [9] or Xsens MVN [18].…”