We present a motion sensor node to support physiotherapy, based on an Inertial Measurement Unit (IMU). The node has wireless interfaces for both data exchange and charging, and is built based on commodity components. It hence provides an affordable solution with a low threshold to technology adoption. We share the hardware design and explain the calibration and validation procedures. The sensor node has an autonomy of 28 h in operation and a standby time of 8 months. On-device sensor fusion yields static results of on average $3.28$° with a drift of 2° per half hour. The final prototype weighs 38 g and measures ø6 cm × $1.5$ cm. The resulting motion sensor node presents an easy to use device for both live monitoring of movements as well as interpreting the data afterward. It opens opportunities to support and follow up treatment in medical cabinets as well as remotely.

The treatment of Musculoskeletal Disorders requires physical exercises to be executed regularly and correctly. This paper presents a technological solution to support this therapy consisting of on-body sensors and a supporting platform that captures and forwards the data automatically. We implemented wireless communication and charging in the sensor module. The wireless functionality enhances patient's comfort during movements and allows for easy and hygienic maintenance. Validation in a real-life case has demonstrated the suitability of the solution to monitor treatment remotely. Furthermore, it was shown to be easy-to-use by non-technical experts. The sensor has an autonomy of 20 hours, which contributes to its user friendliness. We explain and share the designs that have been realized with low cost IoT technologies including Micro-Electro-Mechanical System sensors, low power wireless connectivity and microcontrollers. The contactless multi-sensor solution can be broadly adopted both in medical cabinets and for remote treatment. The latter is of specific interest when the patient is far from the therapist, encounters mobility problems, or when safe distancing is needed.

Channel estimation is essential in massive MIMO systems. Pilot Contamination (PC) however, causes a major bottleneck in the acquisition of this information. The exploitation of the Angle of Arrival (AoA) provides multiple techniques for channel estimation under PC. However, many AoA estimation techniques require information on the signal statistics which is not available in dynamic scenarios. In this paper we propose and analyse the Matrix Pencil Method (MPM) to decorrelate contaminated channels based on their estimated AoA. We evaluate this method both through simulations and experiments in a real-life testbed. Our assessment focuses on a system with a Uniform Linear Array (ULA). The performance of the MPM is validated through simulations ¹ with varying number of antennas, SNR and AoA difference. The results show that our approach effectively decorrelates the channels starting from 20 antennas and an SNR of 15 dB, which outperforms the theoretical expectation. This allows us to enhance the channel estimation quality under PC to the level of no PC. Real-life measurements confirm the simulated results. Our MPM implementation can achieve a target AoA estimation accuracy both with and without PC. We anticipate that the method can be extended for a Uniform Rectangular Array (URA). ¹ We would like to thank NVIDIA for providing the GPU that was used to greatly accelerate our simulations.