Using Xbox kinect motion capture technology to improve clinical rehabilitation outcomes for balance and cardiovascular health in an individual with chronic TBI

Background Motion capture virtual reality-based rehabilitation has become more common. However, therapists face challenges to the implementation of virtual reality (VR) in clinical settings. Use of motion capture technology such as the Xbox Kinect may provide a useful rehabilitation tool for the treatment of postural instability and cardiovascular deconditioning in individuals with chronic severe traumatic brain injury (TBI). The primary purpose of this study was to evaluate the effects of a Kinect-based VR intervention using commercially available motion capture games on balance outcomes for an individual with chronic TBI. The secondary purpose was to assess the feasibility of this intervention for eliciting cardiovascular adaptations. Methods A single system experimental design (n = 1) was utilized, which included baseline, intervention, and retention phases. Repeated measures were used to evaluate the effects of an 8-week supervised exercise intervention using two Xbox One Kinect games. Balance was characterized using the dynamic gait index (DGI), functional reach test (FRT), and Limits of Stability (LOS) test on the NeuroCom Balance Master. The LOS assesses end-point excursion (EPE), maximal excursion (MXE), and directional control (DCL) during weight-shifting tasks. Cardiovascular and activity measures were characterized by heart rate at the end of exercise (HRe), total gameplay time (TAT), and time spent in a therapeutic heart rate (TTR) during the Kinect intervention. Chi-square and ANOVA testing were used to analyze the data. Results Dynamic balance, characterized by the DGI, increased during the intervention phase χ 2 (1, N = 12) = 12, p = .001. Static balance, characterized by the FRT showed no significant changes. The EPE increased during the intervention phase in the backward direction χ 2 (1, N = 12) = 5.6, p = .02, and notable improvements of DCL were demonstrated in all directions. HRe (F (2,174) = 29.65, p = \u3c .001) and time in a TTR (F (2, 12) = 4.19, p = .04) decreased over the course of the intervention phase. Conclusions Use of a supervised Kinect-based program that incorporated commercial games improved dynamic balance for an individual post severe TBI. Additionally, moderate cardiovascular activity was achieved through motion capture gaming. Further studies appear warranted to determine the potential therapeutic utility of commercial VR games in this patient population. Trial registration Clinicaltrial.gov ID - NCT0288928

Wearable Platform for Automatic Recognition of Parkinson Disease by Muscular Implication Monitoring

The need for diagnostic tools for the characterization of progressive movement disorders - as the Parkinson Disease (PD) - aiming to early detect and monitor the pathology is getting more and more impelling. The parallel request of wearable and wireless solutions, for the real-time monitoring in a non-controlled environment, has led to the implementation of a Quantitative Gait Analysis platform for the extraction of muscular implications features in ordinary motor action, such as gait. The here proposed platform is used for the quantification of PD symptoms. Addressing the wearable trend, the proposed architecture is able to define the real-time modulation of the muscular indexes by using 8 EMG wireless nodes positioned on lower limbs. The implemented system “translates” the acquisition in a 1-bit signal, exploiting a dynamic thresholding algorithm. The resulting 1-bit signals are used both to define muscular indexes both to drastically reduce the amount of data to be analyzed, preserving at the same time the muscular information. The overall architecture has been fully implemented on Altera Cyclone V FPGA. The system has been tested on 4 subjects: 2 affected by PD and 2 healthy subjects (control group). The experimental results highlight the validity of the proposed solution in Disease recognition and the outcomes match the clinical literature results

Using the Microsoft Kinect to assess human bimanual coordination

Optical marker-based systems are the gold-standard for capturing three-dimensional (3D) human kinematics. However, these systems have various drawbacks including time consuming marker placement, soft tissue movement artifact, and are prohibitively expensive and non-portable. The Microsoft Kinect is an inexpensive, portable, depth camera that can be used to capture 3D human movement kinematics. Numerous investigations have assessed the Kinect\u27s ability to capture postural control and gait, but to date, no study has evaluated it\u27s capabilities for measuring spatiotemporal coordination. In order to investigate human coordination and coordination stability with the Kinect, a well-studied bimanual coordination paradigm (Kelso, 1984, Kelso; Scholz, & Schöner, 1986) was adapted. ^ Nineteen participants performed ten trials of coordinated hand movements in either in-phase or anti-phase patterns of coordination to the beat of a metronome which was incrementally sped up and slowed down. Continuous relative phase (CRP) and the standard deviation of CRP were used to assess coordination and coordination stability, respectively.^ Data from the Kinect were compared to a Vicon motion capture system using a mixed-model, repeated measures analysis of variance and intraclass correlation coefficients (2,1) (ICC(2,1)).^ Kinect significantly underestimated CRP for the the anti-phase coordination pattern (p \u3c.0001) and overestimated the in-phase pattern (p\u3c.0001). However, a high ICC value (r=.097) was found between the systems. For the standard deviation of CRP, the Kinect exhibited significantly higher variability than the Vicon (p \u3c .0001) but was able to distinguish significant differences between patterns of coordination with anti-phase variability being higher than in-phase (p \u3c .0001). Additionally, the Kinect was unable to accurately capture the structure of coordination stability for the anti-phase pattern. Finally, agreement was found between systems using the ICC (r=.37).^ In conclusion, the Kinect was unable to accurately capture mean CRP. However, the high ICC between the two systems is promising and the Kinect was able to distinguish between the coordination stability of in-phase and anti-phase coordination. However, the structure of variability as movement speed increased was dissimilar to the Vicon, particularly for the anti-phase pattern. Some aspects of coordination are nicely captured by the Kinect while others are not. Detecting differences between bimanual coordination patterns and the stability of those patterns can be achieved using the Kinect. However, researchers interested in the structure of coordination stability should exercise caution since poor agreement was found between systems

Design and implementation of ergonomic risk assessment feedback system for improved work posture assessment

Ergonomic risk factors which include force, repetition and awkward postures, can result in Work-Related Musculoskeletal Disorders (WMSDs) among workers. Hence, systems that provide real-time feedback to the worker concerning his current ergonomic behaviours are desirable. This paper presents the design and implementation of a human-machine interface posture assessment feedback system whose conceptual model is developed through a model-driven development perspective using the Unified Modeling Language (UML) and interface flow diagrams. The resulting system provides a shop floor with a simple, cost-effective and automatic tool for real-time display of worker's postures. Testing the system on volunteer participants reveals that it is easy to use, achieves real-time posture assessment and provides easy-to-understand feedback to workers. This system may be useful for reducing the rate of occurrence of awkward postures, one of the contributing factors to risk of WMSDs among workers

Movements of older adults during exergaming interventions that are associated with the Systems Framework for Postural Control: A systematic review

One in three older adults fall annually, in part due to impairments in the physiological systems that make up the postural control (PC) system. Exercise, particularly balance training, helps to prevent deterioration and even to improve outcomes in the PC system. Exergaming (exercise-gaming) is interactive computer gaming whereby an individual moves the body in response to onscreen cues in a playful format. Exergaming is an alternative method to standard practice for improving PC outcomes, which has been shown to reduce the risk of falling. Exergaming has received research attention, yet the intervention is still in its infancy. There could be benefit in exploring the movements trained with respect to a framework known for identifying underlying deficits in the PC system, the Systems Framework for Postural Control (SFPC). This may help target areas for improvement in balance training using exergames and shed light on the impact for fall prevention. A literature search was therefore conducted across six databases (CINAHL, EMBASE, PubMed, ISI, SPORTdiscus and Science Direct) using a range of search terms and combinations relating to exergaming, balance, exercise, falls and elderly. Quality assessment was conducted using the PEDro Scale and a custom-made quality assessment tool. Movements were rated by two reviewers based on the 9 operational definitions of the SFPC. Eighteen publications were included in the analysis, with a mean PEDro score of 5.6 (1.5). Overall, 4.99 (1.27) of the 9 operational definitions of the SFPC are trained in exergaming interventions. Exergaming does encourage individuals to stand up (3), lean while standing (4), move upper limbs and turn heads (6) and dual-task while standing (9), to some extent move the body forwards, backwards and sideways (1), and coordinate movements (2) but hardly at all to kick, hop, jump or walk (7), or to force a postural reaction from a physical force to the individual (5) and it does not mimic actual changes in sensory context (8). This is the first review, to our knowledge, that synthesises the literature on movements trained in exergaming interventions with respect to an established theoretical framework for PC. This review could provide useful information for designing exergames with PC outcomes in mind, which could help target specific exergames for multi-factorial training to overcome balance deficits. Some elements of PC are too unsafe to be trained using exergames, such as restricting sensory inputs or applying physical perturbations to an individual to elicit postural responses

Using Kinect sensor in observational methods for assessing postures at work

This paper examines the potential use of Kinect range sensor in observational methods for assessing postural loads. Range sensors can detect the position of the joints at high sampling rates without attaching sensors or markers directly to the subject under study. First, a computerized OWAS ergonomic assessment system was implemented to permit the data acquisition from Kinect and data processing in order to identify the risk level of each recorded postures. Output data were compared with the results provided by human observers, and were used to determine the influence of the sensor view angle relative to the worker. The tests show high inter-method agreement in the classification of risk categories (Proportion agreement index = 0.89 k = 0.83) when the tracked subject is facing the sensor. The camera’s point of view relative to the position of the tracked subject significantly affects the correct classification of the postures. Although the results are promising, some aspects involved in the use of low-cost range sensors should be further studied for their use in real environmentsDiego-Mas, JA.; Alcaide Marzal, J. (2014). Using Kinect sensor in observational methods for assessing postures at work. Applied Ergonomics. 1-10. doi:10.1016/j.apergo.2013.12.001S11

Preliminary Validation of a Low-Cost Motion Analysis System Based on RGB Cameras to Support the Evaluation of Postural Risk Assessment

This paper introduces a low-cost and low computational marker-less motion capture system based on the acquisition of frame images through standard RGB cameras. It exploits the open-source deep learning model CMU, from the tf-pose-estimation project. Its numerical accuracy and its usefulness for ergonomic assessment are evaluated by a proper experiment, designed and performed to: (1) compare the data provided by it with those collected from a motion capture golden standard system; (2) compare the RULA scores obtained with data provided by it with those obtained with data provided by the Vicon Nexus system and those estimated through video analysis, by a team of three expert ergonomists. Tests have been conducted in standardized laboratory conditions and involved a total of six subjects. Results suggest that the proposed system can predict angles with good consistency and give evidence about the tool’s usefulness for ergonomist

Integrated Measurement System of Postural Angle and Electromyography Signal for Manual Materials Handling Assessment

Ergonomics practitioners and engineers require an integrated measurement system which allows them to study the interaction of work posture and muscle effort in manual materials handling (MMH) tasks so that strenuous posture and muscle strain can be avoided. However, far too little attention has been paid to develop an integrated measurement system of work posture and muscle activity for assessing MMH tasks. The aim of this study was to develop and test a prototype of integrated system for measuring work posture angles and (electromyography) EMG signals of a worker who doing MMH tasks. The Microsoft Visual Studio software, a 3D camera (Microsoft Kinect), Advancer Technologies muscle sensors and a microcontroller (NI DAQ USB-6000) were applied to develop the integrated postural angle and EMG signal measurement system. Additionally, a graphical user interface was created in the system to enable users to perform work posture and muscle effort assessment simultaneously. Based on the testing results, this study concluded that the patterns of EMG signals are depending on postural angles which consistent with the findings of established works. Further study is required to enhance the validity, reliability and usability of the prototype so that it may facilitate ergonomics practitioners and engineers to assess work posture and muscle effort in MMH task