Overindulgence of harmful substances such as drugs or alcohol, called substance abuse, can directly affect a person's health and their day-to-day activities. The younger population become more vulnerable to such use of psychoactive substances due to lack of awareness of the long-term hazardous effects these substances can have on their health. Additionally, these individuals tend to develop severe mental disorders as they grow older. With the boom of Internet of Things (IoT), the use of wearable sensors such as smartwatches and smartphones has tremendously increased. These wearables help in monitoring a person's physiological signal and keep them informed of one's health. In this research, we propose an edge-intelligent IoT-based wearable that can assist in substance-abuse detection by monitoring their physiological signals on daily basis. The proposed system helps in monitoring the substance abuse and craving of the individual and help the healthcare provider to start an early intervention as required. The proposed system is validated using a custom-built wearable, i-SAD, which was developed as a dedicated substance abuse wearable using commercially available off-the-shelf components. The proposed wearable design was validated using medical quality wearable and yielded a correlation of 0.89 for accelerometer values and 0.92 for average heart rate values.
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Easy-Assist: An Intelligent Haptic-based Affective framework for Assisted Living
Unlike the younger population that uses wearables such as smartwatches for monitoring health on a daily basis, elderly people need assistance in the use of technology and interpreting the data obtained through these smart connected frameworks. The current monitoring systems are primarily designed to monitor the physiological signals on a daily basis. The aim of this proposed research, Easy-Assist, is to help older people to maintain their emotional well-being. This research is focused on developing a wearable affective framework, which can help in detecting the emotions of the user in addition to monitoring their physiological signals. The proposed framework can be used in an automated assisted living environment, where the user's emotional state can be balanced using a haptic-based emotional elicitation system after the user's emotion is recognized, detected and interpreted in real-time. The proposed framework is validated using a fall detection algorithm deployed in a custom-built watch wearable, built using off-the-shelf components and an emotion detection framework built using a single board computer. A dataset of 21700 samples acquired using the proposed framework yielded a maximum efficiency of 97.25%, 96 %, and 94 %, in classifying the state and emotion classes into Alert, Active and Normal classes respectively, using multi-class SVM model. The overall latency of the proposed research was in few orders of milli-seconds.
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- NSF-PAR ID:
- 10157977
- Date Published:
- Journal Name:
- 2020 IEEE International Conference on Consumer Electronics (ICCE)
- Page Range / eLocation ID:
- 1 to 5
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
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- Free Publicly Accessible Full Text
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Accepted Manuscript1.0
- Conference Paper:
- https://doi.org/10.1109/ICCE46568.2020.9042970
