Abstract
Maintaining certain physical activity levels is important to prevent or delay the onset of many medical conditions such as diabetes, or mental health disorders. Traditional calorie estimation methods require wearing devices, such as pedometers, smart watches or smart bracelets, which continuously monitor user activity and estimate the energy expenditure. However, wearable devices may not be suitable for some patients due to the need for periodic maintenance, frequent recharging and having to wear it all the time. In this paper we investigate a feasibility of a device- free human energy expenditure estimation based on RF-sensing, which recognises coarse-grained user activity, such as walking, standing, sitting or resting by monitoring the impact of a person’s activity on ambient wireless links. The calorie estimation is then based on Metabolic Equivalent concept that expresses the energy cost of an activity as a multiple of a person’s basal metabolic rate using Harrison-Benedict model. The experimental evaluation using low cost IEEE 802.15.4 transceivers demonstrated that the approach estimated energy expenditure within an indoor environment within 7.4% to 41.2% range when compared to a FitBit Blaze bracelet.
| Original language | English |
|---|---|
| Title of host publication | nan |
| Publisher | Institute of Electrical and Electronics Engineers Inc. |
| DOIs | |
| Publication status | Published - 12 Oct 2020 |
| Event | The 16th International Conference on Wireless and Mobile Computing, Networking and Communications (WiMob 2020) - Thessaloniki Duration: 12 Oct 2020 → 14 Oct 2020 http://www.wimob.org/wimob2020/index.html |
Conference
| Conference | The 16th International Conference on Wireless and Mobile Computing, Networking and Communications (WiMob 2020) |
|---|---|
| City | Thessaloniki |
| Period | 12/10/20 → 14/10/20 |
| Other | The 16th International Conference on Wireless and Mobile Computing, Networking and Communications (WiMob 2020) (12/10/2020-14/10/2020, Thessaloniki) |
| Internet address |
UN SDGs
This output contributes to the following UN Sustainable Development Goals (SDGs)
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SDG 3 Good Health and Well-being
Keywords
- Internet of Things (IoT)
- IoT
- machine learning
- wireless
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