The use of videogames and motion capture systems in rehabilitation contributes to the recovery of the patient. This systematic review aimed to explore the works related to these technologies. The PRISMA method (Preferred Reporting Items for Systematic reviews and Meta-Analyses) was used to search the databases Scopus, PubMed, IEEE Xplore, and Web of Science, taking into consideration four aspects: physical rehabilitation, the use of videogames, motion capture technologies, and upper limb rehabilitation. The literature selection was limited to open access works published between 2015 and 2020, obtaining 19 articles that met the inclusion criteria. The works reported the use of inertial measurement units (37%), a Kinect sensor (48%), and other technologies (15%). It was identified that 26% used commercial products, while 74% were developed independently. Another finding was that 47% of the works focus on post-stroke motor recovery. Finally, diverse studies sought to support physical rehabilitation using motion capture systems incorporating inertial units, which offer precision and accessibility at a low cost. There is a clear need to continue generating proposals that confront the challenges of rehabilitation with technologies which offer precision and healthcare coverage, and which, additionally, integrate elements that foster the patient’s motivation and participation.

This paper presents the application of polymer optical fiber (POF) sensors in wearable devices for gait assistance. Intensity variation-based POF curvature sensor for the acquisition of joint angles and a POF insole based to detect gait events were developed. Three wearable devices were employed for the practical validation of the developed sensors: an active orthosis with one degree of freedom, a robotic exoskeleton and a functional electrical stimulation (FES) system for gait assistance. POF is a viscoelastic material, which does not have a constant response to stress or strain and also can present hysteresis in dynamic measurements. In order to mitigate these effects, a compensation technique based on the polymer viscoelastic features was applied on both curvature sensor and instrumented insole. The results obtained for the POF curvature sensor on the knee joint angle measurement of the orthosis and exoskeleton show root mean squared error (RMSE) of 3.7° and 3.8°, respectively. Regarding the POF insole results, it was used to identify five gait events in the FES system application. The reported results pave the way for new instrumentation approaches in wearable devices with flexible sensors that can be especially desirable in the rigid robots presented, but also in soft robotics applications.

In this paper, we explore the combined use of inertial sensors and the Kinect for applications on rehabilitation robotics and assistive devices. In view of the deficiencies of each individual system, a new method based on Kalman filtering was developed in order to perform online calibration of sensor errors automatically whenever measurements from Kinect are available. The method was evaluated on experiments involving healthy subjects performing multiple DOF tasks.

This paper presents an algorithm to perform online tremor characterization from motion sensors measurements, while filtering the voluntary motion performed by the patient. In order to estimate simultaneously both nonstationary signals in a stochastic filtering framework, pathological tremor was represented by a time-varying harmonic model and voluntary motion was modeled as an auto-regressive moving-average (ARMA) model. Since it is a nonlinear problem, an extended Kalman filter (EKF) was used. The developed solution was evaluated with simulated signals and experimental data from patients with different pathologies. Also, the results were comprehensively compared with alternative techniques proposed in the literature, evidencing the better performance of the proposed method. The algorithm presented in this paper may be an important tool in the design of active tremor compensation systems.

This paper provides a general simulation study on tremor based on a modular neuromusculoskeletal model. It focuses on the peripheral mechanism. It is known that the reflex loops in the peripheral nervous system have influences on the tremor. A neuromusculoskeletal model with several reflex loops is developed to explore the dynamics of tremor. The muscle model is derived from a Hill-type muscle model. The reflex loops include the spindle organ, Golgi tendon organ, and Renshaw cell. Their effects are investigated quantitatively in detail. A two-muscle (agonist/antagonist) system with interaction is further studied. Moreover, a model in combination with the central oscillation and peripheral system is developed. Some results are in accordance with the previous research, whereas some new findings are proposed according to the simulation study.