HemoKinect: A Microsoft Kinect V2 Based Exergaming Software to Supervise Physical Exercise of Patients with Hemophilia

Abstract

Patients with hemophilia need to strictly follow exercise routines to minimize their risk of suffering bleeding in joints, known as hemarthrosis. This paper introduces and validates a new exergaming software tool called HemoKinect that intends to keep track of exercises using Microsoft Kinect V2’s body tracking capabilities. The software has been developed in C++ and MATLAB. The Kinect SDK V2.0 libraries have been used to obtain 3D joint positions from the Kinect color and depth sensors. Performing angle calculations and center-of-mass (COM) estimations using these joint positions, HemoKinect can evaluate the following exercises: elbow flexion/extension, knee flexion/extension (squat), step climb (ankle exercise) and multi-directional balance based on COM. The software generates reports and progress graphs and is able to directly send the results to the physician via email. Exercises have been validated with 10 controls and eight patients. HemoKinect successfully registered elbow and knee exercises, while displaying real-time joint angle measurements. Additionally, steps were successfully counted in up to 78% of the cases. Regarding balance, differences were found in the scores according to the difficulty level and direction. HemoKinect supposes a significant leap forward in terms of exergaming applicability to rehabilitation of patients with hemophilia, allowing remote supervision.

Assessment of Kinect V2 for elbow range of motion estimation in people with haemophilia using an angle correction model

“…HemoKinect allows to evaluate a series of exercises that patients with haemophilia can perform at home. In addition, this software generates reports that can be sent to the physiotherapist remotely …”

“…Motion capture sensors such as Kinect V2 with dedicated software could favour the assessment, monitoring and remote care of patients with haemophilia. 13 To date, some works report the use of clinical goniometers to validate Kinect V1 and study its usability for virtual rehabilitation. 14,15 Most of them conclude that Kinect V1 has limitations in accuracy for the measurement of specific joint motions.…”

Deep Learning-Based Upper Limb Functional Assessment Using a Single Kinect v2 Sensor

“…The network is trained with a batch size of 20 and the learning rate is set to 0.006 for 200 epochs. In application, the upper limb kinematics k Φ Sensors 2020, 20,1903 To train this model, we let be the input of the model. The output is denoted by…”

“…The Kinect SDK v2.0 features skeletal tracking with 3D locations of 25 joints for each skeleton [12]. Kinect v2 has been employed in gait analysis [13][14][15], balance and postural assessment [16,17], foot position tracking [18], gait rehabilitation training [19,20], upper limb functional assessment or rehabilitation training [4,[21][22][23][24][25].Several studies have assessed the agreement between Kinect sensor and 3DMC. Kinect sensor demonstrated good reliability in assessing temporal-spatial parameters such as timing, velocity, or movement distance of functional tasks for both healthy subjects and people with physical disorders [4,13,21,22,26,27].…”

“…The Kinect SDK v2.0 features skeletal tracking with 3D locations of 25 joints for each skeleton [12]. Kinect v2 has been employed in gait analysis [13][14][15], balance and postural assessment [16,17], foot position tracking [18], gait rehabilitation training [19,20], upper limb functional assessment or rehabilitation training [4,[21][22][23][24][25].…”