Gait analysis is a technique that is used to understand movement patterns and, in some cases, to inform the development of rehabilitation protocols. Traditional rehabilitation approaches have relied on expert guided feedback in clinical settings. Such efforts require the presence of an expert to inform the re-training (to evaluate any improvement) and the patient to travel to the clinic. Nowadays, potential opportunities exist to employ the use of digitized "feedback" modalities to help a user to "understand" improved gait technique. This is important as clear and concise feedback can enhance the quality of rehabilitation and recovery. A critical requirement emerges to consider the quality of feedback from the user perspective i.e. how they process, understand and react to the feedback. In this context, this paper reports the results of a Quality of Experience (QoE) evaluation of two feedback modalities: Augmented Reality (AR) and Haptic, employed as part of an overall gait analysis system. The aim of the feedback is to reduce varus/valgus misalignments, which can cause serious orthopedics problems. The QoE analysis considers objective (improvement in knee alignment) and subjective (questionnaire responses) user metrics in 26 participants, as part of a within subject design. Participants answered 12 questions on QoE aspects such as utility, usability, interaction and immersion of the feedback modalities via post-test reporting. In addition, objective metrics of participant performance (angles and alignment) were also considered as indicators of the utility of each feedback modality. The findings show statistically significant higher QoE ratings for AR feedback. Also, the number of knee misalignments was reduced after users experienced AR feedback (35% improvement with AR feedback relative to baseline when compared to haptic). Gender analysis showed significant differences in performance for number of misalignments and time to correct valgus misalignment (for males when they experienced AR feedback). The female group self-reported higher utility and QoE ratings for AR when compared to male group.A QoE assessment of haptic and augmented reality feedback modalities in a gait analysis system PLOS ONE | https://doi.This section contains a critique of related research in terms of multimodal gait feedback systems and QoE assessments approaches for Haptic and AR (not all are specific to gait feedback). Each of these aspects are relevant to the scope of this work. PLOS ONEA QoE assessment of haptic and augmented reality feedback modalities in a gait analysis system PLOS ONE | https://doi.
Motion analysis is a technique used by clinicians (among many others) that quantifies human movement by using camera-based systems. Marker-based motion analysis systems have been used across a variety of application domains, from Interactive 3D Tele-Immersion (i3DTI) environments to the diagnosis of neuromuscular and musculoskeletal diseases. Although such analysis is performed in several laboratories in many countries, numerous issues exist: (1) the high cost of precise motion capture systems; (2) scarcity of qualified personnel to operate them; (3) expertise required to interpret their results; (4) space requirements to install and store these systems; (5) complexity in terms of measurement protocol required for such systems; (6) limited availability; (7) and in some situations the use of markers means they are unsuitability for certain clinical use cases (e.g. for patients recovering from orthopaedic surgery). In this paper, we present, from a system perspective, an alternative, cheaper, and more accessible system for motion analysis. The ultimate aim is to use the output of this multimodal marker-less system as part of an immersive multimedia gait re-education tool. In real-time, it will advise the user on their gait performance (as well as potentially providing accurate clinical data to clinicians). With the initial focus on the capture system, we have developed and evaluated a novel multimodal system which integrates Multiple Microsoft Kinects (which employ RGB-D cameras) with multiple Shimmer Inertial Measurement Unit (IMU) sensors. We have compared this system with the VICON system (the gold standard in motion capture). Our marker-less motion capture system combines data from 4 skeletons generating 3D and complete 360 degrees in view skeleton. The system combines unit quaternions from each Kinect joint with quaternions from 4 inertial measurement units to promote integration. We used our system to measure 3D points of 12 joints from the Kinect fused skeleton and flexion-extension angles of the knee and hip in a walking trial in 8 participants with 8-10 trials per participant. The analysis found component similarity of 0.97 for knee angles and 0.98 for hip angles. These results show that our system, through combination of Multi Kinect system and Shimmer IMUs, offers a cheaper, sufficiently accurate and more accessible human motion analysis system.
Gait analysis is the measurement, processing and systematic interpretation of biomechanical parameters that characterize human locomotion. It supports the identification of movement limitations and development of rehabilitation procedures. Accurate Gait analysis is important in sports analysis, medical field, and rehabilitation. Although Gait analysis is performed in several laboratories in many countries, there are many issues such as: (i) the high cost of precise Motion Capture systems; (ii) the scarcity of qualified personnel to operate them; (iii) expertise required to interpret their results; (iv) space requirements to install and store these systems; as well as difficulties related to the measurement protocols of each system; (vi) limited availability (vii) and the use of markers can be a barrier for some clinical use cases (e.g. patients recovering from orthopedics surgeries). In this work, we present a low cost and more accessible system based on the integration of a Multiple Microsoft Kinect sensors and multiple Shimmer inertial sensors to capture human Gait. The novel multimodal system combines data from inertial and 3D depth cameras and outputs spatiotemporal Gait variables. A comparison of this system with the VICON system (the gold standard in Motion Capture) was performed. Our relatively low-cost marker-less multimodal motion generates a complete 360-degree skeleton view. We compare our system with the VICON via gait spatiotemporal variables: Gait cycle time, stride time, Gait length (distance between two strides), stride length, and velocity. The system was also evaluated with knee and hip joint angles measurement accuracy. The results show high correlation for spatiotemporal variables and joint angles inside the 95% bootstrap prediction when compared with VICON.
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