Motor Rehabilitation of Upper Limbs Using a Gesture-Based Serious Game: Evaluation of Usability and User Experience.

Objective: Gesture-based serious games can be based on playful and interactive scenarios to enhance user engagement and experience during exercises, thereby increasing efficiency in the motor rehabilitation process. This study aimed to develop the Rehabilite Game (RG) as a complementary therapy tool for upper limb rehabilitation in clinics and home environments and to evaluate aspects of usability and user experience of it. Materials and Methods: The evaluation consisted of the use of a gesture-based serious game with motor rehabilitation sessions managed in a web platform. Thirty-three participants were recruited (21 physiotherapists and 12 patients). The protocol allowed each participant to have the experience of playing sessions with different combinations of settings. The User Experience Questionnaire (UEQ) was used to evaluate aspects of usability and user experience. The study was approved by the Research Ethics Board of the Federal University of Piaui (number 3,429,494). Results: The level of satisfaction with the RG was positive, with an excellent Net Promoter Score for 85.7% of physiotherapists and 100% of patients. All six UEQ scales (attractiveness, perspicuity, efficiency, dependability, stimulation, and novelty) reflected acceptance. Conclusion: The study demonstrated that, according to the results obtained in the experiments, the RG had positive feedback from physiotherapists and patients, indicating that the game can be used in a clinical trial to be compared with other rehabilitation techniques.

A computer vision-based mobile tool for assessing human posture: A validation study.

BACKGROUND AND OBJECTIVE: Non-invasive methods for postural assessment are tools used for tracking and monitoring the progression of postural deviations. Different computer-based methods have been used to assess human posture, including mobile applications based on images and sensors. However, such solutions still require manual identification of anatomical points. This study aims to present and validate the NLMeasurer, a mobile application for postural assessment. This application takes advantage of the PoseNet, a solution based on computer vision and machine learning used to estimate human pose and identify anatomical points. From the identified points, NLMeasurer calculates postural measures. METHODS: Twenty participants were photographed in front view while using surface markers over anatomical landmarks. Then, the surface markers were removed, and new photos were taken. The photos were analyzed by two examiners, and six postural measurements were computed with NLMeasurer and a validated biophotogrammetry software. One-sample t-test and Bland Altman procedure were used to assess agreement between the methods, and Intraclass Correlation Coefficient (ICC) was used to assess inter- and intra-rater reliability. RESULTS: Postural measurements calculated using the NLMeasurer were in agreement with the biophotogrammetry software. Furthermore, there was good inter- and intra-rater reliability for most photos without surface markers. CONCLUSIONS: NLMeasurer demonstrated to be a valid tool method to assess postural measurements in the frontal view. The use of surface markers on specific anatomical landmarks (i.e., ears, iliac spines and ankles) can facilitate the digital identification of these landmarks and improve the reliability of the postural measurements performed with NLMeasurer.

Can human posture and range of motion be measured automatically by smart mobile applications?

Human posture and Range of Motion (ROM) are important components of a physical assessment and, from the collected data, it is possible to identify postural deviations such as scoliosis or joint and muscle limitations, hence identifying risks of more serious injuries. Posture assessment and ROM measures are also necessary metrics to monitor the effect of treatments used in the motor rehabilitation of patients, as well as to monitor their clinical progress. These evaluation processes are more frequently performed through visual inspection and manual palpation, which are simple and low cost methods. These methods, however, can be optimized with the use of tools such as photogrammetry and goniometry. Mobile solutions have also been developed to help health professionals to capture more objective data and with less risk of bias. Although there are already several systems proposed for assessing human posture and ROM in the literature, they have not been able to automatically identify and mark Anatomical and Segment Points (ASPs). The hypothesis presented here considers the development of a mobile application for automatic identification of ASPs by using machine learning algorithms and computer vision models associated with technologies embedded in smartphones. From ASPs identification, it will be possible to identify changes in postural alignment and ROM. In this context, our view is that an application derived from the hypothesis will serve as an additional tool to assist in the physical assessment process and, consequently, in the diagnosis of disorders related to postural and movement changes.