Even though technology-aided personal navigation is an extensively studied research topic, approaches based on inertial sensors remain challenging. In this study, the authors present a comparison between different inertial systems, investigating the impacts of on-body placement of Inertial Measurement Units (IMUs) and, consequently, of different algorithms for the estimation of the travelled path on the navigation accuracy. In particular, the system performance is investigated considering two IMU placements: (i) on the feet and (ii) on the lower back. Sensor fusion is then considered in order to take advantage of the strengths of each placement. The results are validated through an extensive data collection in indoor and outdoor environments.

Limb length is a useful parameter in the assessment of common musculoskeletal disorders such as limb length discrepancy. The measurement variation among rates adversely affects the quantitative aspect of assessments and introduces a greater subjectivity in the course of treatment. Common practise for measuring limb length is based on radiographic imaging techniques which are inconvenient, costly and require clinical knowledge. Direct instruments are difficult to use with patients due to susceptibility to human error in determining the position of the rotational joint. In this study, the determination of limb length is automated using a contemporary algorithm which applies curvature to the measurements from a low-cost and miniaturised inertial sensor, primarily used in the bio-kinematic research. The motion artefacts contribute to the ultimate estimations and, in this approach, a least noise threshold model is employed to address the robustness. The proposed estimation technique was validated with real-data observed from 14 healthy subjects comparing with radiographic and direct measurements. The experimental results indicate greater accuracy compared with manual measurements with low root mean squared error percentages with values ranging from 5.34 to 5.84%. Additionally, the mean limb length difference between our estimator and both radiographic measurements and direct measurement was <1.6 cm.

The reduction of energy consumption and the successful delivery of data are important for a wireless body sensor network (WBSN). Many studies have been performed to improve energy efficiency, but most of them have not focused on the biosensor nodes in the WBSN bottleneck zone. Energy consumption is a critical issue in WBSNs, as the nodes that are placed next to the sink node consume more energy. All biomedical packets are aggregated through these nodes forming a bottleneck zone. This study proposes a novel mathematical model for body area network topology to explain the deployment and connection between biosensor nodes, simple relay nodes, network coding (NC) relay nodes and the sink node. Therefore, this study is dedicated to research both the energy saving and delivery of data if there is a failure in one of the links of the transmission, which relates to the proposed random linear NC model in the WBSN. Using a novel mathematical model for the WBSN, it is apparent that energy consumption is reduced and data delivery achieved with the proposed mechanism. This study details the stages of the research work.

The key features of wireless body area networks are limited energy resources of the sensor nodes and the need for highly reliable packet transmission. Therefore, designing a suitable algorithm that schedules transmission of the nodes is very important. Although an optimal algorithm has been previously reported based on a partially observable Markov decision process (POMDP), its complexity is high. In this study, the authors propose a suboptimal algorithm for scheduling the transmissions, i.e. a semi-myopic algorithm, with a performance close to the optimal, albeit with much lower complexity. To this end, they modify the structure of the cost function of the optimal algorithm such that the scheduling decision at each superframe is made regarding its effect on only a few upcoming superframes. The authors' proposed algorithm is considered in two different incoming packet scenarios, deterministic and random arrivals. Simulation results show that for both types of arrivals, there is a negligible difference between their proposed algorithm and the optimal one, i.e. POMDP, in terms of energy consumption and reliability.

This study presents a low-power energy-harvesting circuit with error-correction demodulation for body sensor networks with radio-frequency identification (RFID) systems. The proposed circuit adopts a new structure that facilitates the demodulation of amplitude-shift-keying (ASK) signals, and it includes a power unit consisting of a wake-up circuit and a power-on-reset control circuit. The wireless bio-signal acquisition system and the digital processor are implemented on a field-programmable gate array to demonstrate the proposed RFID tag. The proposed energy-harvesting circuit can substantially decrease the capacitance used by the demodulator circuit and the wake-up circuit to as low as 14 pF. Moreover, a detection circuit and a correcting circuit are utilised to reduce the bit error rate of decoding. The wireless bio-signal acquisition scenario is also set up and the communication signal between reader and tag is measured to demonstrate the system that can be employed in body sensor network with the healthcare system. The chip was implemented in complementary metal–oxide–semiconductor 0.18 μm technology. The sensitivity of this work is − 13 dBm. The pulse width error can be reduced to <1% by the error-correction demodulator to fit the EPC Gen-2 standard.