They also have potential security problems as their signal cannot be stopped from propagating into free-space. Moreover, radio-wave propagation technologies are prone to interference with adjacent communication links since most of them, such as Bluetooth, operate at the busy 2.4 GHz, the industrial, scientific, and medical (ISM) band 37, 38. It results in higher power consumption, shorter battery life, and lower reliability 33, 35, 36. This technique is susceptible to the characteristics of the environment, and its signal experiences a high attenuation around a lossy medium, such as the human body.
The conventional state-of-the-art wireless sensor networks working in the vicinity of the human body adopt radio-wave propagation for signal transmission. Since wireless communication consumes a considerable portion of the energy 30, numerous studies have proposed and investigated low-power solutions 31, 32, 33, 34. Due to the limitation of energy resources, the power management has become a critical issue in designing a WBAN. On the other hand, frequent battery recharging may not be practical for sensor networks with multiple sensors in applications such as senior monitoring 7. Wearable devices must be small and lightweight, which puts a restriction on the battery size and longevity. Moreover, the system should guarantee the security and privacy of the user’s data. To ensure users’ safety, it has to satisfy specific absorption ratio (SAR) constraints, while providing a reliable wireless link 29. It has to operate under proper guidelines limiting the power exposure to the user since the energy absorption may lead to temperature elevation in biological tissues. For example, the system has to be inexpensive, accessible to the general public, and meet ergonomic constraints and health requirements. However, WBAN design is challenging as many constraining, and often conflicting, requirements have to be taken into account 26, 27, 28. This approach can provide comprehensive information on the mobility of body segments and potentially improve system accuracy. Then data are transmitted wirelessly to a central processing unit for detection. In WBANs, sensors are spatially distributed over the human body and collect data from the user. Wireless body area network (WBAN) consisting of wearable devices operating around the human body can tackle these problems 21, 25. Additionally, in systems relying on data from a single device, variations in position can have a significant effect on the performance or lead to the failure of the monitoring system 20, 23, 24. For example, inertial sensors embedded in a smartwatch cannot capture the movement of legs, which restricts the capability of the system in classifying activities. A single wearable cannot cover the entire body and therefore fails to obtain adequate information about the mobility of all body segments 20, 21, 22. Although these devices provide a privacy-aware alternative solution that overcomes many disadvantages of the external approach, they still might not be able to address the requirements of a diverse range of applications. Several research studies have reported the use of smartwatches and smartphones in human activity monitoring, and have presented a satisfactory performance 16, 17, 18, 19. Recent advances in embedded sensor technology have made it feasible to monitor the user’s activity using smart devices. In the second approach, on-body sensors, such as accelerometers, gyroscopes, and magnetometers, are used to translate human motion into signal patterns for activity recognition 13, 14, 15. Additionally, cameras cannot capture any data if the user performs out of their reach 11, 12. It requires infrastructure support, such as the installation of video cameras in surveillance areas, which is usually costly. However, it faces many challenges in terms of coverage, accuracy, privacy, and cost. The vision-based technique, for example, is one of the well-known external methods that has been extensively studied for human activity analysis 9, 10. In the external approach, the monitoring devices are set at fixed points, and users are expected to interact with them 8. Two main approaches for deployment of HAR systems are external and wearable sensors 7. It allows computer systems to assist users with their tasks and to improve the quality of life in areas such as senior care, rehabilitation, daily life-logging, personal fitness, and assistance for people with cognitive disorders 1, 2, 3, 4, 5, 6. Human activity recognition (HAR) aims to provide information on human physical activity and to detect simple or complex actions in a real-world setting.