A 3D Spine and Full Skeleton Model for Opto-Electronic Stereo- Photogrammetric Multi-Sensor Biomechanical Analysis in Posture and Gait

Quantitative functional evaluation of spine is highly desirable in posture and movement analysis. Given the complexity of the spine biomechanical system, very few studies outline the behaviour of the spine in posture and movement analysis. During a research lasting 25 years, a complete three‐dimensional (3D) parametric biomechanical skeleton model including a 3D full spine model based on the measurements of the positions of suitable body landmarks labelled by passive markers has been implemented. Around this model, a fully dedicated 3D opto‐electronic stereo‐photogrammetric system named Global Opto‐electronic Approach for Locomotion and Spine (GOALS) has been developed. Depending on different analysis purposes, the model can work at different stages of complexity. The model can integrate seamlessly data deriving from multiple measurement devices, such as 3D stereo‐photogrammetric systems, force platforms, surface electro‐myography and foot pressure maps. In addition to single‐trial analysis, the possibility to assess and to extract mean behaviours either for posture or for cyclical tasks (e.g. multiple strides in gait) has been included. The aim of this paper is to describe the current level of development of the GOALS system and its versatility as a clinical tool. To this purpose, examples of multi‐factorial quantitative functional descriptions of paradigmatic cases are presented

Radar and RGB-depth sensors for fall detection: a review

This paper reviews recent works in the literature on the use of systems based on radar and RGB-Depth (RGB-D) sensors for fall detection, and discusses outstanding research challenges and trends related to this research field. Systems to detect reliably fall events and promptly alert carers and first responders have gained significant interest in the past few years in order to address the societal issue of an increasing number of elderly people living alone, with the associated risk of them falling and the consequences in terms of health treatments, reduced well-being, and costs. The interest in radar and RGB-D sensors is related to their capability to enable contactless and non-intrusive monitoring, which is an advantage for practical deployment and users’ acceptance and compliance, compared with other sensor technologies, such as video-cameras, or wearables. Furthermore, the possibility of combining and fusing information from The heterogeneous types of sensors is expected to improve the overall performance of practical fall detection systems. Researchers from different fields can benefit from multidisciplinary knowledge and awareness of the latest developments in radar and RGB-D sensors that this paper is discussing

SANE (Easy Gait Analysis System): Towards an AI-Assisted Automatic Gait-Analysis

The gait cycle of humans may be influenced by a range of variables, including neurological, orthopedic, and pathological conditions. Thus, gait analysis has a broad variety of applications, including the diagnosis of neurological disorders, the study of disease development, the assessment of the efficacy of a treatment, postural correction, and the evaluation and enhancement of sport performances. While the introduction of new technologies has resulted in substantial advancements, these systems continue to struggle to achieve a right balance between cost, analytical accuracy, speed, and convenience. The target is to provide low-cost support to those with motor impairments in order to improve their quality of life. The article provides a novel automated approach for motion characterization that makes use of artificial intelligence to perform real-time analysis, complete automation, and non-invasive, markerless analysis. This automated procedure enables rapid diagnosis and prevents human mistakes. The gait metrics obtained by the two motion tracking systems were compared to show the effectiveness of the proposed methodology

Kinect-based Solution for the Home Monitoring of Gait and Balance in Elderly People with and without Neurological Diseases

Alterations of gait and balance are a significant cause of falls, injuries, and consequent hospitalizations in the elderly. In addition to age-associated motor decline, other factors can impact gait and stability, including the motor dysfunctions caused by neurological diseases such as Parkinson’s disease or hemiplegia after stroke. Monitoring changes and deterioration in gait patterns and balance is crucial for activating rehabilitation treatments and preventing serious consequences. This work presents a Kinect-based solution, suitable for domestic contexts, for assessing gait and balance in individuals at risk of falling. The system captures body movements during home acquisition sessions scheduled by clinicians at definite times of the day and automatically estimates specific functional parameters to objectively characterize the subjects’ performance. The system includes a graphical user interface designed to ensure usability in unsupervised contexts: the human-computer interaction mainly relies on natural body movements to support the self-management of the system, if the motor conditions allow it. This work presents the system’s features and facilities, and the preliminary results on healthy volunteers’ trials