PurposeHuman 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.Design/methodology/approachThis 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.FindingsThe 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.Originality/valueThe 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 medical advancement in recent years is addressing challenges of the dependent people like senior citizens, physically challenged, and cognitively impaired individuals by providing technical aids to promote a healthier society. The radical improvement in the digital world is trying to make their life smoother by creating a smart living environment via ambient assisted living (AAL) rather than hospitalization. In this chapter, an Edge-based AAL-IoT ecosystem is introduced with the prime objective of delivering telehealthcare to elderly and telerehabilitation to disabled individuals. The proposed framework focuses on developing smart home, an intelligent atmosphere for real-time monitoring in regard to meet the needs of independent and isolated individuals. The supporting technologies to leverage the edge computing concept, to enable scalability and reliability are also studied. A case study on proposed architecture for quarantined patient monitoring remotely in the event of epidemic or pandemic diseases is presented.

The medical advancement in recent years is addressing challenges of the dependent people like senior citizens, physically challenged, and cognitively impaired individuals by providing technical aids to promote a healthier society. The radical improvement in the digital world is trying to make their life smoother by creating a smart living environment via ambient assisted living (AAL) rather than hospitalization. In this chapter, an Edge-based AAL-IoT ecosystem is introduced with the prime objective of delivering telehealthcare to elderly and telerehabilitation to disabled individuals. The proposed framework focuses on developing smart home, an intelligent atmosphere for real-time monitoring in regard to meet the needs of independent and isolated individuals. The supporting technologies to leverage the edge computing concept, to enable scalability and reliability are also studied. A case study on proposed architecture for quarantined patient monitoring remotely in the event of epidemic or pandemic diseases is presented.