Virtual reality (VR) has emerged as a valid addition to conventional therapy in rehabilitation and sports medicine. This has enabled the development of novel and affordable rehabilitation strategies. However, before VR devices can be used in these situations, they must accurately capture the range of motion of the body-segment where they are mounted. This study aims to state the accuracy of the Oculus Touch v2 controller when used to measure the elbow’s motion in the sagittal plane. The controller is benchmarked against an inertial sensor (ENLAZA$$^{\mathrm{TM}}$$ TM ), which has already been validated as a reliable measurement device. We have developed a virtual environment that matches both the Oculus Touch v2 and the inertial sensor orientations using a digital goniometer. We have also collected the orientation measurements given by each system for a set of 17 static angles that cover the full range of normal elbow flexion and hyperextension motion, in 10° intervals from − 10°  (hyperextension) to 150° (flexion). We have applied the intra-rater reliability test to assess the level of agreement between the measurements of these devices, obtaining a value of 0.999, with a 95% confidence interval ranged from 0.996 to 1.000. By analyzing the angle measurement outcomes, we have found that the accuracy degrades at flexion values between 70° and 110°, peaking at 90°. The accuracy of Oculus Touch v2 when used to capture the elbow’s flexion motion is good enough for the development of VR rehabilitation applications based on it. However, the flaws in the accuracy that have been revealed in this experimental study must be considered when designing such applications.

Patients with upper limb disorders are limited in their activities of daily living and impose an important healthcare burden due to the repetitive rehabilitation they require. A way to reduce this burden is through home-based therapy using virtual reality solutions, since they are readily available, provide immersion, and enable accurate motion tracking, and custom applications can be developed for them. However, there is lack of guidelines for the design of effective VR rehabilitation applications in the literature, particularly for bimanual training. This work introduces a VR telerehabilitation system that uses off-the-shelf hardware, a real-time remote setup, and a bimanual training application that aims to improve upper extremity motor function. It is made of six activities and was evaluated by five physiotherapists specialised in (2) neuromotor disorders and (3) functional rehabilitation and occupational therapy. A descriptive analysis of the results obtained from the System Usability Scale test of the application and a collection of qualitative assessments of each game have been carried out. The application obtained a mean score of 86.25 (±8.96 SD) in the System Usability Scale, and the experts concluded that it accurately reproduces activities of daily living movements except for wrist and finger movements. They also offer a set of design guidelines.

Virtual reality (VR) applications on rehabilitation a home-base exercise experiences have boomed in the last decade. This is mainly because their entertainment capacity creates a sense of immersion in the users, which enhances adherence to their use. In addition, offering body-related visual feedback is a proven approach to the physical training towards a goal. Recent literature showed the exercise of pedalling has the potential to provide a high number of flexion and extension repetitions of the lower limb in reasonable therapeutic time periods to improve muscle activity, strength and balance in elders, but also motor improvements in patients with neurological injuries. The objective of this work is to present a low-cost wireless application in virtual reality (VR) for pedalling exercises. The platform developed consists of a VR headset and an inertial measurement unit (IMU). The VR headset processes the kinematic information of the IMU to estimate the cadence of the pedalling, while the IMU sensor tracks the angle of hip flexion/extension movement of the user. In order to confirm the suitability of this cadence estimation system, our approach is confronted with a cycling platform developed and validated in a previous study. In the present study, we carried out two repeated sessions with 13 subjects at 3 set speeds: slow (30 rpm), medium (60 rpm) and fast (90 rpm). The Spearman’s correlation (PC) between both systems for the 3 speeds and sessions shows high correlation values for low and medium speeds and moderate correlation for high speed. The SEM results for each system show low measurement error (about 1 cycle) for both systems at every target speed, except for the virtual cycling platform at the highest speed (SEM of VCP at 90 rpm = 3.24 cycles). The repeatability analysis based on ICC (3, 1) absolute agreement shows consistency in all measurements for both systems at high speed and also reflects the irregularity in measurements at low and medium speeds, where participants were less stable during testing due to entertainment from the VR system. All in all, it is concluded the validity of the cadence estimation system for pedalling exercises with low intensity. This development allows us to control the virtual environment by adapting the visual stimulus to cycling cadence. The proposed system can generate sensitive inputs to influence the user’s pedalling cadence.