# Movement Analysis for Health and Biometrics
###### Abstract  
The analysis of human movement provides important insights in several fields, such as biomechanics, neuroscience, psychology, medicine, and Artificial Intelligence (AI).  
The recent advancements in modeling and analyzing movements are contributing to both the understanding of the neurological and physical principles underlying movement learning and execution [1, 2] and the development of new applications in healthcare and biometrics [3, 4]. In particular, the availability of low-cost and pervasive devices for recording movements (wearable devices, smartphones, tablets, cameras, etc.), together with machine learning methods for the quantitative and automatic analysis of movement, has put forward the development of systems for user authentication, medical diagnosis, and rehabilitation monitoring.  
Studying human movements requires choosing both the motor tasks to be analyzed and the equipment to record movements. These choices are taken keeping in mind which aspects of movement are of interest, and the final application. Different motor tasks are investigated in the literature (gait, reaching movements, handwriting, etc.) and recorded with different devices (motion tracker, tablet, smartwatch, etc.).  
The Special Issue \"Movement analysis for health and biometrics\" includes 10 articles and 1 brief report that highlight how movement analysis and new technologies are innovating the fields of medical monitoring and disease diagnosis, action and therapy monitoring, and injury prevention. The instrumentation adopted in the 11 papers includes a 3D Motion Capture System with Inertial Sensors, wearable sensors, and tablets. The motor tasks investigated are handwriting, push and pull tasks, upper-limb movements, walking and running, standing and turning, and other movement patterns.  
The remaining of this editorial is organised as follows. Section 2 resumes the findings of the articles focused on the diagnosis and monitoring of musculoskeletal and neurodegenerative diseases, Section 3 summarizes the findings of the studies aimed at preventing injuries via the analysis of movement, while Section 4 describes the only article on human activity recognition. Finally, Section 5 concludes this editorial by highlighting the perspective of human movement science.  
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features, measured during the execution of specific handwriting tasks. According to the obtained results the authors provide a set of guidelines for the design of a diagnostic tool for the early detection of PD and some suggestions for reducing motor impairments in PD patients.  
[PERSON] et al. [8] develop a novel indicator of upper limb manipulative movements, the Upper Body Index (UBI). The index is constructed by analyzing and comparing the kinematics of the upper limb of two groups, (a) healthy subjects and (b) people that had previously suffered an ischemic stroke. The proposed index enables the discovering of deviations from the standard performance of upper limb movements. Therefore, the index may be applicable to the analysis of any sequence of upper limb movements and used for providing some cues about its dysfunctions for suggesting further clinical investigations.  
[PERSON] and [PERSON] [9] investigate the motor performance of individuals with mild stages of PD and healthy subjects while performing non-steady-state circuit trials comprising stairs, ramps, and changes of direction. Applying a linear discriminant analysis classifier and a Long-Short Term Memory neural network, their analysis provides some insights into developing advanced frameworks for healthcare monitoring and lower-limb assistive devices.  
[PERSON] et al. [10] investigate the balance in terms of changes in body kinematics and muscle activity. The study involves two elderly participants, a healthy subject and a subject with chronic stroke. The study proposes a novel protocol that tests and validates the hypothesis that training improves gait and balance by increasing joint angles and extensor muscle activities in the lower extremities of elderly people.