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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.

Sleep quality has been associated with health and safety issues influencing construction labour productivity in developing countries. Despite its significantly adverse contribution to these facets if left unattended to, limited studies have sought to establish its prevalence and causal factors in labour-intensive contexts. This study aims to bridge the gap between the prevalence and casual factors of poor sleep quality among construction workers.

Adopting a case study research design, data were collected from a randomly selected sample of construction workers (n = 475) recruited from four construction-based corporations in Southern India. Self-administered questionnaires comprising a Pittsburgh Sleep Quality Index (PSQI) scale was used to measure sleep quality among respondents alongside demographic characteristics, lifestyle preferences and work-habits data. Data were analysed using multivariate logistic regression analysis and descriptive statistical techniques.

Slightly more than a third of the construction workers (n = 33.9%) surveyed experienced poor sleep quality based on a PSQI method score of = 5. Also, the nexus between demographic characteristics, lifestyle preferences and work habits on the incidence of poor sleep quality among construction workers was confirmed.

The study contributes to the contemporary discourse on improving sleep health of construction workers to enhance their well-being thereby enabling their contribution towards achieving improved construction labour productivity.

This study makes an original contribution to the extent that it seeks to not only determine the prevalence of sleep quality within the construction industry and associated causal factors but to explore its implications on construction labour productivity.