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Human gait analysis is based on a significant part of the musculoskeletal, nervous and respiratory systems. Gait analysis is widely adopted to help patients increase community involvement and independent living.

This paper presents a system for the classification of abnormal human gaits using a Markerless 3D Motion Capture device. This study aims at examining and estimating the spatiotemporal and kinematic parameters obtained by 3D gait analysis in diverse groups of gait-impaired subjects and compares the parameters with that of healthy participants to interpret the gait patterns.

The classification is based on mathematical models that distinguish between normal and abnormal gait patterns depending on the deviations in the gait parameters. The difference between the gait measures of the control and each disease group was examined using 95% limits of agreement by the Bland and Altman method. The scatter plots demonstrated gait variability in Parkinsonian and ataxia gait and knee joint angle variation in hemiplegic gait when compared with those of healthy controls. To prove the validity of the Kinect camera, significant correlations were detected between Kinect- and inertial-based gait tests.

The various techniques used for gait assessments are often high in price and have existing limitations like the hindrance of components. The results suggest that the Kinect-based gait assessment techniques can be used as a low-cost, less-intrusive alternative to expensive infrastructure gait lab tests in the clinical environment.

The purpose of this paper is to model and predict suitable gait trajectories of lower-limb exoskeleton for wearer during rehabilitation walking. Lower-limb exoskeleton is widely used for assisting walk in rehabilitation field. One key problem for exoskeleton control is to model and predict suitable gait trajectories for wearer.

In this paper, the authors propose a Deep Spatial-Temporal Model (DSTM) for generating knee joint trajectory of lower-limb exoskeleton, which first leverages Long-Short Term Memory framework to learn the inherent spatial-temporal correlations of gait features.

With DSTM, the pathological knee joint trajectories can be predicted based on subject’s other joints. The energy expenditure is adopted for verifying the effectiveness of new recovery gait pattern by monitoring dynamic heart rate. The experimental results demonstrate that the subjects have less energy expenditure in new recovery gait pattern than in others’ normal gait patterns, which also means the new recovery gait is more suitable for subject.

Long-Short Term Memory framework is first used for modeling rehabilitation gait, and the deep spatial–temporal relationships between joints of gait data can obtained successfully.

The purpose of this paper is to put forward a nvew reconfigurable multi-mode walking-rolling robot based on the single-loop closed-chain four-bar mechanism, and the robot can be changed to different modes according to the terrain.

Based on the topological analysis, singularity analysis, feasibility analysis, gait analysis and the motion strategy based on motor time-sharing control, the paper theoretically verified that the robot can switch between the four motion modes.

The robot integrates four-bar walking, self-deforming and four-bar and six-bar rolling modes. A series of simulation and prototype experiment results are presented to verify the feasibility of multiple motion modes of the robot.

The work presented in this paper provides a good theoretical basis for further exploration of multiple mode mobile robots. It is an attempt to design the multi-mode mobile robot based on single loop kinematotropic mechanisms. It is also a kind of exploration of the new unknown movement law.

Many studies have shown that rehabilitation robots are crucial for lower limb dysfunction, but application of many robotics have yet to be seen to actual use in China. This study aimed to improve a lower limb rehabilitation robot by details improving and practical design.

Structures and control system of a lower limb rehabilitation robot are improved in detail, including joint calculations, comfort analysis and feedback logic creation, and prototype experiments on healthy individuals and patients are conducted in a hospital.

All participating subjects did not experience any problems. The experiment shows detail improving is reasonable, and feasibility of the robot was confirmed, which has potential for overcoming difficulties and problems in practical application.

Therapeutic effects need to be evaluated in the future. Also, more details should be improved continuously based on the actual demand.

The improved robot could assist the lower limb during standing or walking, which has significance for practical application and patients in China.

The purpose of this study is to see whether the figure of eight walkings (F8W) is a reliable outcome measure in rehabilitation care especially for the older population who need extensive consideration in their practical functional affairs.

The literature review was conducted by researching various databases such as Google Scholar, Pub Med and Web of Science, Scopus. Journals with Good impact factors were included in this study.

This review suggested that F8W could be the realistic outcome measure for rehabilitation in patients with musculoskeletal and neurological dysfunctions that could identify disability and functional impairments more deeply. Future studies in this field may provide further exploration in physiotherapy rehabilitation.

The study was a short review with limited resources. Its’ findings and outputs must be considered prelusive.

The accustomed routine of manhood accounts for constant twists and turns which is entirely incompatible with a linear walk. In such a manner, walking in the figure of eight could detect the major perplexity faced by an individual in his habitual pursuing affairs predominately in orthopedics and neurological disorders.

The study has suggested the utility of F8W as a reliable tool in the rehabilitation of the elder population, evidence suggests, F8W constitutes curvy and straight paths, which could trace troubles while turning and maintaining coordination. These results of this study can serve as a foundation for future studies.

This pragmatic research paper aims to unravel the smart vision-based method (SVBM), an AI program to correlate the computer vision (recorded and live videos using mobile and embedded cameras) that aids in manual lifting human pose deduction, analysis and training in the construction sector.

Using a pragmatic approach combined with the literature review, this study discusses the SVBM. The research method includes a literature review followed by a pragmatic approach and lab validation of the acquired data. Adopting the practical approach, the authors of this article developed an SVBM, an AI program to correlate computer vision (recorded and live videos using mobile and embedded cameras).

Results show that SVBM observes the relevant events without additional attachments to the human body and compares them with the standard axis to identify abnormal postures using mobile and other cameras. Angles of critical nodal points are projected through human pose detection and calculating body part movement angles using a novel software program and mobile application. The SVBM demonstrates its ability to data capture and analysis in real-time and offline using videos recorded earlier and is validated for program coding and results repeatability.

Literature review methodology limitations include not keeping in phase with the most updated field knowledge. This limitation is offset by choosing the range for literature review within the last two decades. This literature review may not have captured all published articles because the restriction of database access and search was based only on English. Also, the authors may have omitted fruitful articles hiding in a less popular journal. These limitations are acknowledged. The critical limitation is that the trust, privacy and psychological issues are not addressed in SVBM, which is recognised. However, the benefits of SVBM naturally offset this limitation to being adopted practically.

The theoretical and practical implications include customised and individualistic prediction and preventing most posture-related hazardous behaviours before a critical injury happens. The theoretical implications include mimicking the human pose and lab-based analysis without attaching sensors that naturally alter the working poses. SVBM would help researchers develop more accurate data and theoretical models close to actuals.

By using SVBM, the possibility of early deduction and prevention of musculoskeletal disorders is high; the social implications include the benefits of being a healthier society and health concerned construction sector.

Human pose detection, especially joint angle calculation in a work environment, is crucial to early deduction of muscoloskeletal disorders. Conventional digital technology-based methods to detect pose flaws focus on location information from wearables and laboratory-controlled motion sensors. For the first time, this paper presents novel computer vision (recorded and live videos using mobile and embedded cameras) and digital image-related deep learning methods without attachment to the human body for manual handling pose deduction and analysis of angles, neckline and torso line in an actual construction work environment.

Motion capture system (MoCap) has been used in measuring the human body segments in several applications including film special effects, health care, outer-space and under-water navigation systems, sea-water exploration pursuits, human machine interaction and learning software to help teachers of sign language. The purpose of this paper is to help the researchers to select specific MoCap system for various applications and the development of new algorithms related to upper limb motion.

This paper provides an overview of different sensors used in MoCap and techniques used for estimating human upper limb motion.

The existing MoCaps suffer from several issues depending on the type of MoCap used. These issues include drifting and placement of Inertial sensors, occlusion and jitters in Kinect, noise in electromyography signals and the requirement of a well-structured, calibrated environment and time-consuming task of placing markers in multiple camera systems.

This paper outlines the issues and challenges in MoCaps for measuring human upper limb motion and provides an overview on the techniques to overcome these issues and challenges.

WHEN the British Productivity Council launched its scheme in March for setting up local productivity committees in many parts of this country it was emphasised by Mr. Butler, Chancellor of the Exchequer, that there must be a great expansion of trade at competitive prices. It would require courage, drive and vision, he said. Although he was speaking in what is normally regarded as the close season for Chancellors it did seem then that he was conscious of the need for providing industry with a tangible incentive to expand production.

Screening for fall risks is an important part of fall and fracture prevention. This study aims to investigate cross-sectional inter-instrumental agreement and participants’ preferences of the self-rated Falls Risk Questionnaire (FRQ) and Activities Specific Balance Confidence 6 items (ABC-6). This study also aimed to compare FRQ and ABC-6 scores in older adults with and without a history of falls.

Through an online and snowball sampling survey, 114 respondents were recruited from six countries. Respondents were asked to perform FRQ and ABC-6 surveys.

The mean respondent age was 67 years, and 44.8% reported falls in the past year. The mean of rescored FRQ and ABC-6 scores were 68.6% and 66.2%, respectively. The FRQ and ABC-6 scores for fallers were lower than non-fallers. Bland and Altman’s method indicated the mean −2.6 and two standard deviations 20.9 differences between ABC-6 and FRQ, which means an overall agreement between these tools. Most of the respondents, 36% had no preference between ABC-6 and FRQ, 34% preferred none, 21% preferred the ABC-6 and 9% preferred the FRQ for screening future falls risk.

Both ABC-6 and FRQ can distinguish between fallers and non-fallers, and findings of this study can be used to support the use of the FRQ for falls screening in older adults.

This paper aims to present the application and critical reflection on the effects of a intervention for children with autism spectrum disorder (ASD): the Soundbeam Imitation Intervention (SII). The intervention is based on the imitation of meaningless body gestures supported by a musical feedback. The rationale underlying SII is that mirror neurons deficit may represent the cause for the incomplete development of social and motor functioning in children with ASD. Following this assumption, it is possible to hypothesise that a systematic activation of this a system through the simultaneous observation-execution of meaningless body gestures may affect functional changes of mirror-related functions.

A sample of 14 children, who were between 5 and 9 years of age, with a diagnosis of ASD were involved in a six weeks’ SII programme. The programme is designed as a three-step progression, where each step includes exercises that focus on an activity: synchronous/one arm imitation, synchronous/two arms imitation and delayed imitation. Exercises are based on repeated movements-melodies associations of increasing difficulty. Motor imitation and social attention were assessed using a synchronous video-modelling task pre and post intervention.

Data highlight significant improvements in imitation accuracy and duration of social sustained attention were achieved.

Data reported in this paper provide preliminary and promising evidence that imitation and social attention skills acquired through SII can be generalised to a video-modelling imitation setting. The SII ordinal execution has included meaningless gestures, usually excluded from previous interventions, and this adds further validity to the training.