The use of sensor technology in sports facilitates the data-driven evaluation of human movement not only in terms of quantity but also in terms of quality. This scoping review presents an overview of sensor technologies and human movement quality assessments in ecologically-similar environments. We searched four online databases to identify 16 eligible articles with either recreational and/or professional athletes. A total of 50% of the studies used inertial sensor technology, 31% vision-based sensor technology. Most of the studies (69%) assessed human movement quality using either the comparison to an expert’s performance, to an exercise definition or to the athletes’ individual baseline performance. A total of 31% of the studies used expert-based labeling of the movements to label data. None of the included studies used a control group-based study design to investigate impact on training progress, injury prevention or behavior change. Although studies have used sensor technology for movement quality assessment, the transfer from the lab to the field in recreational and professional sports is still emerging. Hence, research would benefit from impact studies of technology-assisted training interventions including control groups as well as investigating features of human movement quality in addition to kinematic parameters.

Inertial measurement units (IMU) are widely used in sports applications to digitise human motion by measuring acceleration and rotational velocity in three-dimensional space. A common machine learning problem is the classification of human motion primitives from IMU data. In order to investigate the classification methods used in the existing literature and to analyse whether and how the time-dependent data structure is considered in the classification process of motion data analysis in sports, a scoping review was conducted. Based on a keyword search, articles from 2010 to 2021 were extracted, and 93 articles were relevant for data extraction. Over- and undersampling of data and data augmentation techniques were rarely used. The classification methods applied can be divided into three main branches: classic machine learning and deep learning models, threshold-based approaches, and dynamic time warping. The most often applied algorithms were support vector machines (SVM), followed by neural networks and k-nearest neighbours. In comparative works, when more than one classifier was applied, random forests, neural networks, boosting models and SVM were found to be the methods that achieved the highest accuracy. If the time-dependent data structure was taken into account, it was incorporated either within the models, for example, by using long-short-term memory models or within the feature calculation step by using rolling windows with an overlap, which was the most common method of considering the time dependency of the IMU data.

Many existing motion sensing applications in research, entertainment and exercise monitoring are based on the Microsoft Kinect and its skeleton tracking functionality. With the Kinect’s development and production halted, researchers and system designers are in need of a suitable replacement. We investigated the interchangeability of the discontinued Kinect v2 and the all-in-one, image-based motion tracking system Orbbec Persee for the use in an exercise monitoring system prototype called ILSE. Nine functional training exercises were performed by six healthy subjects in front of both systems simultaneously. Comparing the systems’ internal tracking states from ’not tracked’ to ‘tracked’ showed that the Persee system is more confident during motion sequences, while the Kinect is more confident for hip and trunk joint positions. Assessing the skeleton tracking robustness, the Persee’s tracking of body segment lengths was more consistent. Furthermore, we used both skeleton datasets as input for the ILSE exercise monitoring including posture recognition and repetition-counting. Persee data from exercises with lateral movement and in uncovered full-body frontal view provided the same results as Kinect data. The Persee further preferred tracking of quasi-static lower limb motions and tight-fitting clothes. With these limitations in mind, we find that the Orbbec Persee is a suitable replacement for the Microsoft Kinect for motion sensing within the ILSE exercise monitoring system.

Background: Hiking is one of the most popular forms of exercise in the alpine region. However, besides its health benefits, hiking is the alpine activity with the highest incidence of cardiac events. Most incidents occur due to overexertion or underestimation of the physiological strain of hiking. Objective: This project will establish a standardized cardio trekking test trail to evaluate the exercise capacity of tourists within hiking areas and deliver a tool for the prevention of hiking-associated cardiac incidents. Further, individual exercise intensity for a hiking tour will be predicted and visualized in digital maps. Methods: This cooperation study between Austria and Germany will first validate a 1-km outdoor cardio trekking test trail at 2 different study sites. Then, exercise intensity measures on 8-km hiking trails will be evaluated during hiking to estimate overall hiking intensity. A total of 144 healthy adults (aged >45 years) will perform a treadmill test in the laboratory and a 1-km hiking test outdoors. They will wear a portable spirometry device that measures gas exchange, as well as heart rate, walking speed, ventilation, GPS location, and altitude throughout the tests. Estimation models for exercise capacity based on measured parameters will be calculated. Results: The project “Connect2Move” was funded in December 2019 by the European Regional Development Fund (INTERREG V-A Programme Austria-Bavaria – 2014-2020; Project Number AB296). “Connect2Move” started in January 2020 and runs until the end of June 2022. By the end of April 2022, 162 participants were tested in the laboratory, and of these, 144 were tested outdoors. The data analysis will be completed by the end of June 2022, and results are expected to be published by the end of 2022. Conclusions: Individual prediction of exercise capacity in healthy individuals with interest in hiking aims at the prevention of hiking-associated cardiovascular events caused by overexertion. Integration of a mathematical equation into existing hiking apps will allow individual hiking route recommendations derived from individual performance on a standardized cardio trekking test trail. Trial Registration: ClinicalTrails.gov NCT05226806; https://clinicaltrials.gov/ct2/show/NCT05226806 International Registered Report Identifier (IRRID): DERR1-10.2196/39038

Background: Borg’s rating of perceived exertion (BRPE) scale is a simple, but subjective tool to grade physical strain during exercise. As a result, it is widely used for the prescription of exercise intensity, especially for cardiovascular disease prevention. The purpose of this study was to assess and compare relationships between BRPE and physiological measures of exercise intensity during uphill walking indoors and outdoors. Methods: 134 healthy participants [median age: 56 years (IQR 52–63)] completed a maximal graded walking test indoors on a treadmill using the modified Bruce protocol, and a submaximal 1 km outdoor uphill cardio-trekking test (1 km CTT). Heart rate (HR) and oxygen consumption (V̇O₂) were continuously measured throughout both tests. BRPE was simultaneously assessed at the end of each increment on the treadmill, while the maximal BRPE value was noted at the end of the 1 km CTT. Results: On the treadmill, BRPE correlated very high with relative HR (%HR_max) (ρ = 0.88, p < 0.001) and V̇O₂ (%V̇O_2max) (ρ = 0.89, p < 0.001). During the 1 km CTT, a small correlation between BRPE and %HR_max (ρ = 0.24, p < 0.05), respectively %V̇O_2max was found (ρ = 0.24, p < 0.05). Conclusions: Criterion validity of BRPE during uphill walking depends on the environment and is higher during a treadmill test compared to a natural environment. Adding sensor-based, objective exercise-intensity parameters such as HR holds promise to improve intensity prescription and health safety during uphill walking in a natural environment.

Background: To empower healthy aging, digital solutions embed multiple modules for physical activity, cognitive health promotion, and social engagement. Integrating new empowering technologies such as digital exercise monitoring requires assessment measures and analysis procedures, considering variable compliance of users with different modules. Objective: This study aims to assess the influence of a tablet-based and a feedback system–based exercise module on balance and leg strength by considering use adherence instead of the use of the entire multimodular system. Methods: In the prospective cohort study within the fit4AAL project, 83 users (n=67, 81% women; n=16, 19% men; mean age 66.2, SD 2.3 years) used the 2 digital exercise modules of a multimodular physical activity promotion system for >18 weeks. A data-driven clustering method based on the average use frequency of the exercise modules determined the number of user types that met the World Health Organization–recommended training frequency of at least twice per week. On the basis of this use adherence, statistical analysis was performed with features of functional performance tests (unipedal stance, 30-second chair rise, Y-balance, and hurdle step tests). The tests were conducted 6 months before the intervention, immediately before the intervention, and after the intervention, comparing the baseline phase with the 3 feedback use groups of the study (using only the tablet, the tablet and the feedback system, or only the feedback system). Results: Of the 83 users, 43 (52%) met the World Health Organization–recommended frequency of muscle-strengthening activities. Overall, the feedback use groups achieved, on average, more chair rises in 30 seconds than the baseline group (P=.01; moderate effect size of 0.07). Of the 43 users, 26 (60%) additionally used the feedback system–based exercise module. They improved in balance compared with the users using either the tablet or the feedback system (P=.02). In addition, they improved their leg strength within the group (P=.04) and compared with the baseline (P=.01). Conclusions: The additional use of a feedback system showed a tendency to positively maintain and influence the already exceptionally high functional performance of older adults. Considering use adherence in future multimodular system studies is crucial to assess the influence of single and combined use of exercise modules on functional performance.