Motion Capture for Telemedicine: A Review of Nintendo Wii, Microsoft Kinect, and PlayStation Move

Access to healthcare has been and continues to be difficult for many around the world. With the introduction of telemedicine, this impediment to attaining medical care has been lifted. Although many avenues of telemedicine exist (and have yet to exist), the use of home video game consoles such as the Nintendo Wii®, Microsoft Kinect®, and PlayStation Move® can be used to measure patient progress outside of the office. Due to the nature of each individual console/system, some unique characteristics exist that allow each system to provide its own clinical potential. A comparative analysis of the clinical implications of the Nintendo Wii®, Microsoft Kinect®, and PlayStation Move® showed that with its ease of use and dynamic accuracy, the Microsoft Kinect® offered the most benefit. With further exploration, using the Microsoft Kinect® for telemedicine will be able to improve medical efficiency and hopefully health outcomes

Markerless motion capture for 3D human model animation using depth camera

3D animation is created using keyframe based system in 3D animation software such as Blender and Maya. Due to the long time interval and the need of high expertise in 3D animation, motion capture devices were used as an alternative and Microsoft Kinect v2 sensor is one of them. This research analyses the capabilities of the Kinect sensor in producing 3D human model animations using motion capture and keyframe based animation system in reference to a live motion performance. The quality, time interval and cost of both animation results were compared. The experimental result shows that motion capture system with Kinect sensor consumed less time (only 2.6%) and cost (30%) in the long run (10 minutes of animation) compare to keyframe-based system, but it produced lower quality animation. This was due to the lack of body detection accuracy when there is obstruction. Moreover, the sensor’s constant assumption that the performer’s body faces forward made it unreliable to be used for a wide variety of movements. Furthermore, standard test defined in this research covers most body parts’ movements to evaluate other motion capture system

Metodología para la medida de la energía consumida en las maniobras de acceso y salida de turismos empleando el sistema de captura de movimiento Kinect

El proyecto que se describe en este artículo pretende desarrollar una metodología que permita medir el gasto energético cuando una persona entra y sale de un vehículo. Para la captura de las maniobras que realiza la persona se emplea la especificación más reciente del sistema de captura de movimiento Kinect. La medida del gasto energético se plantea como una forma de evaluar la accesibilidad a las plazas de un vehículo y está especialmente orientado a personas mayores y personas con movilidad reducida en general. En primer lugar se evalúa la capacidad del sistema de captura de movimiento Kinect en lo referente a precisión en el seguimiento de las diferentes articulaciones del cuerpo que incorpora el modelo tanto en escenarios despejados como en presencia de obstáculos visuales. A continuación, su capacidad para captar las maniobras que se realizan para entrar al habitáculo y subsanar los problemas que plantea el trabajar con un habitáculo de dimensiones más o menos reducidas y con elementos de morfologías diversas como los asientos y el salpicadero. En paralelo, se trabaja en una metodología que permita obtener información sobre el gasto energético de una persona a la hora de realizar las maniobras de acceso y salida que se capturen. La unión de la valoración energética del movimiento y de la captura del mismo con la fiabilidad necesaria debe confluir en la metodología para valorar energéticamente la accesibilidad de un turismo

Clinical assessment of depth sensor based pose estimation algorithms for technology supervised rehabilitation applications

Encouraging rehabilitation by the use of technology in the home can be a cost-effective strategy, particularly if consumer-level equipment can be used. We present a clinical qualitative and quantitative analysis of the pose estimation algorithms of a typical consumer unit (Xbox One Kinect), to assess its suitability for technology supervised rehabilitation and guide development of future pose estimation algorithms for rehabilitation applciations. We focused the analysis on upper-body stroke rehabilitation as a challenging use case. We found that the algorithms require improved joint tracking, especially for the shoulder, elbow and wrist joints, and exploiting temporal information for tracking when there is full or partial occlusion in the depth data

Key body pose detection and movement assessment of fitness performances

Motion segmentation plays an important role in human motion analysis. Understanding the intrinsic features of human activities represents a challenge for modern science. Current solutions usually involve computationally demanding processing and achieve the best results using expensive, intrusive motion capture devices. In this thesis, research has been carried out to develop a series of methods for affordable and effective human motion assessment in the context of stand-up physical exercises. The objective of the research was to tackle the needs for an autonomous system that could be deployed in nursing homes or elderly people's houses, as well as rehabilitation of high profile sport performers. Firstly, it has to be designed so that instructions on physical exercises, especially in the case of elderly people, can be delivered in an understandable way. Secondly, it has to deal with the problem that some individuals may find it difficult to keep up with the programme due to physical impediments. They may also be discouraged because the activities are not stimulating or the instructions are hard to follow. In this thesis, a series of methods for automatic assessment production, as a combination of worded feedback and motion visualisation, is presented. The methods comprise two major steps. First, a series of key body poses are identified upon a model built by a multi-class classifier from a set of frame-wise features extracted from the motion data. Second, motion alignment (or synchronisation) with a reference performance (the tutor) is established in order to produce a second assessment model. Numerical assessment, first, and textual feedback, after, are delivered to the user along with a 3D skeletal animation to enrich the assessment experience. This animation is produced after the demonstration of the expert is transformed to the current level of performance of the user, in order to help encourage them to engage with the programme. The key body pose identification stage follows a two-step approach: first, the principal components of the input motion data are calculated in order to reduce the dimensionality of the input. Then, candidates of key body poses are inferred using multi-class, supervised machine learning techniques from a set of training samples. Finally, cluster analysis is used to refine the result. Key body pose identification is guaranteed to be invariant to the repetitiveness and symmetry of the performance. Results show the effectiveness of the proposed approach by comparing it against Dynamic Time Warping and Hierarchical Aligned Cluster Analysis. The synchronisation sub-system takes advantage of the cyclic nature of the stretches that are part of the stand-up exercises subject to study in order to remove out-of-sequence identified key body poses (i.e., false positives). Two approaches are considered for performing cycle analysis: a sequential, trivial algorithm and a proposed Genetic Algorithm, with and without prior knowledge on cyclic sequence patterns. These two approaches are compared and the Genetic Algorithm with prior knowledge shows a lower rate of false positives, but also a higher false negative rate. The GAs are also evaluated with randomly generated periodic string sequences. The automatic assessment follows a similar approach to that of key body pose identification. A multi-class, multi-target machine learning classifier is trained with features extracted from previous motion alignment. The inferred numerical assessment levels (one per identified key body pose and involved body joint) are translated into human-understandable language via a highly-customisable, context-free grammar. Finally, visual feedback is produced in the form of a synchronised skeletal animation of both the user's performance and the tutor's. If the user's performance is well below a standard then an affine offset transformation of the skeletal motion data series to an in-between performance is performed, in order to prevent dis-encouragement from the user and still provide a reference for improvement. At the end of this thesis, a study of the limitations of the methods in real circumstances is explored. Issues like the gimbal lock in the angular motion data, lack of accuracy of the motion capture system and the escalation of the training set are discussed. Finally, some conclusions are drawn and future work is discussed