Exergames have been proposed as a potential tool to improve the current practice of musculoskeletal rehabilitation. Inertial or optical motion capture sensors are commonly used to track the subject’s movements. However, the use of these motion capture tools suffers from the lack of accuracy in estimating joint angles, which could lead to wrong data interpretation. In this study, we proposed a real time quaternion-based fusion scheme, based on the extended Kalman filter, between inertial and visual motion capture sensors, to improve the estimation accuracy of joint angles. The fusion outcome was compared to angles measured using a goniometer. The fusion output shows a better estimation, when compared to inertial measurement units and Kinect outputs. We noted a smaller error (3.96°) compared to the one obtained using inertial sensors (5.04°). The proposed multi-sensor fusion system is therefore accurate enough to be applied, in future works, to our serious game for musculoskeletal rehabilitation.

A fast computation and accurate analytical model for off-body propagation is derived in this paper. The paper discusses the off-body model propagation from an external source to a receiver located on the body. The model is developed for normal incident plane wave by describing the human body with a circular cylinder. We show that the total received electric field around the human body can be written as a creeping wave in the shadow region and as a geometrical optics result for the lit region. It is also shown that at 60 GHz, the shadow boundary width is negligible. The model shows perfect agreement with the experimental results conducted on a perfectly conducting cylinder. Measurements of the creeping wave path gain have been also conducted on a real body to assess the validity of the cylinder assumption. The results have shown a path gain of about 5 dB/cm for the TM case and 3 dB/cm for the TE case. The standard deviation between the measurements and the cylindrical model is about 3.5 dB for both TM and TE cases.

Recently, new emerging techniques of neuromorphic hardware render spiking neuron networks (SNN) promising as an energy-efficient solution for artificial intelligence (AI). With the idea of physics informed neural network, the structure can be simple while training data can be light. However its application in RF telecommunication system is still challenging. This paper, as the first time in the literatures, proposes a solution of SNN-based digital predistortion (SNN-DPD) for linearization of RF transmitters, such as power amplifiers (PA). A two-layer SNN is deployed in frequency domain to process the spectrum of the stimulus for a predistorted signal. The proposed technique is experimentally validated on a test bench with a real PA of different bias voltages. We also test the proposed SNNDPD for multi-band linearization. The proposed method reaches the best performance of traditional DPD methods while owing advantages of the SNN, such as low power consumption and good biomimicry for AI.

Biomedical connected objects like kinematic sensors have been commonly used for patient monitoring in many clinical applications. Moreover, serious games have become widely used to improve patients‘ motivation during functional rehabilitation. In this work, we developed and evaluated a new engineering system as a solution for functional rehabilitation at home. A multi-sensor fusion between Kinect camera and inertial sensors was developed to animate a 3D avatar during rehabilitation and to estimate kinematic data of different joints for clinical monitoring. Two serious game scenarios were designed for upper and lower limb rehabilitation. The developed system was evaluated through patient kinematic data and a questionnaire-based approach with a panel of eight post-stroke patients and four clinical experts. The evaluation of the system showed that multi-sensor fusion provides useful data for clinical follow-up. The virtual game scenarios lead to a high level of immersion for patients. Feedbacks from clinical experts concerning the system's GUIs and the clinical relevance of the acquired data for each rehabilitation session are positive. The developed system paves the way to deploy recent technologies, such as multi-sensor fusion and serious games, as a solution for home-based rehabilitation, which can optimize the benefit of the involved patients and medical experts.