According to statistics, one in every three adults ageing 65 or older falls every year. Every fall may lead to long-term consequences due to fractures or even neurological damages. These consequences have severe impact in their quality of life, independence and confidence, ultimately increasing the risk of early death. Moreover, the risk of falling increases as age advances. Fortunately, several studies reveal that specific exercise programmes may help in reducing the risk of falling if performed correctly and frequently. However, user engagement and adherence to these programmes are still low mainly due to motivational factors, since interventions are usually long, unadapted and unchallenging. In this paper, a new solution is presented, which uses the concept of interactive games using motion sensors to tackle low adherence (through gaming motivation) and help in physical rehabilitation and reduce fall risk on elderly people by improving balance, muscle strength and mobility. It is intended to be used in community or domestic unsupervised contexts and supports relatively inexpensive sensing equipment (currently Kinect R , Leap Motion R , Orbotix Sphero R and Smartphones) and common platforms (desktop and mobile). Tests were already undertaken with several individuals ageing 65 or more and the results were analysed and discussed, being generally positive, despite some issues in the movement detection algorithms.

All over the world there are millions of people who are living with long-term motor impairments caused by a stroke or any other kind of corticospinal tract injuries. The physical rehabilitation of these patients is usually slow and demotivating. In this paper we introduce Kinteract, a novel solution that applies the paradigm of using motion-based games in the rehabilitation process, with the added value of providing a motion sensor server that supports a growing array of motion sensors (currently Microsoft Kinect, Leap Motion and Orbotix Sphero) and merge their data into a single protocol that can be used for any purpose. The use of different sensors, even at the same time, allows the rehabilitation of specific parts of the body. This data can be stored in a server for physicians to analyse and can clearly reveal the evolution of the patient in the rehabilitation process.

This study aimed to (i) characterise the body composition of professional and semi-professional male futsal players, (ii) assess the validity of commonly used equations to estimate FM%, (iii) develop and cross-validate a futsal-specific FM% prediction equation. In a cross-sectional design, 78 adult male futsal players were assessed for body mass, stature, skinfolds, and girths as per the International Society for the Advancement of Kinanthropometry protocol and completed a dual-energy X-ray absorptiometry (DXA) scan for reference body composition data. Using paired-sample t-tests, the FM% from the DXA and nine published equations were compared. New sport-specific models were developed by stepwise multiple regression. Existing equations were cross-validated using the least squares regression, concordance correlation coefficient, and the Bland–Altman analyses. New equations were further cross-validated using the PRESS approach. None of the existing equations accurately predicted the DXA-derived FM% (p < 0.001; R2 ≤ 0.76, SEE ≥ 1.59; CCC ≤ 0.83; bias = −8.2% to −1.3%, limited agreement, and varying trends). The novel Bettery® equation: −0.620 + (0.159 ∗ Σ4SKF [triceps, abdominal, iliac crest, and front thigh (mm)]) + (0.120 ∗ waist girth (cm)), demonstrated a high accuracy (R2 = 0.85, SEE = 1.32%), a moderate strength of agreement (CCC = 0.92), no bias (0.2%), good agreement (±2.5%), and no trend (r = −0.157; p = 0.170) against the DXA. The Bettery® equation is the first to allow for a valid and sport-specific assessment of FM% in male futsal players.

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