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.

Endurance running is among the most popular physical activities partially due to its low barriers to entry. However, some people avoid running because of respiratory distress, and respiratory monitoring could help prevent this. Wearable sensors are valuable for respiration detection during exercise and enable respiratory feedback in real time. Therefore, this study presents a wearable chest-mounted stride and respiration sensor including step and flow reversal event detection algorithms. The algorithms were evaluated using precision and recall between detected and reference events with respect to different levels of breathing depth, motion artifact, thoracic skin temperature and sweat. Overall F1 scores reached 93.2%, 97.4% and 97.2% for step, expiration and inspiration events, respectively. No significant effect on event detection performance was observed for breathing depth, stride motion artifact, or thoracic skin temperature. In contrast, sweat level slightly decreased detection performance. Consequently, this sensor is able to accurately measure stride and respiration during running and could be suitable for use as a system to guide runners’ respiration during exercise.

Inpatient gait analysis is an essential part of rehabilitation for foot amputees and includes the ground contact time (GCT) difference of both legs as an essential component. Doctors communicate improvement advice to patients regarding their gait pattern based on a few steps taken at the doctor’s visit. A wearable sensor system, called Suralis, consisting of an inertial measurement unit (IMU) and a pressure measuring sock, including algorithms calculating GCT, is presented. Two data acquisitions were conducted to implement and validate initial contact (IC) and toe-off (TO) event detection algorithms as the basis for the GCT difference determination for able-bodied and prosthesis wearers. The results of the algorithms show a median GCT error of −51.7 ms (IMU) and 14.7 ms (sensor sock) compared to the ground truth and thus represent a suitable possibility for wearable gait analysis. The wearable system presented, therefore, enables a continuous feedback system for patients and, above all, a remote diagnosis of spatio-temporal aspects of gait behaviour based on reliable data collected in everyday life.

Endurance running’s popularity stems from its accessibility, but some people avoid it due to respiratory issues. Breathing monitoring using body area networks can help address these concerns by detecting breathing during exercise and offering real-time guiding feedback. This review outlines the challenges in developing body area networks and algorithms to enable intuitive real-time breathing guidance to enhance restful running experiences. Key findings are that existing breathing guidance systems’ feedback is mostly based on instructing breathing rates calculated on past observations. However, detecting instantaneous breathing phase and amplitude allows concurrent breathing guidance by triggering the manipulation of coinciding breathing phases, e.g. towards active prolonged exhales. Aspects of personal breathing guidance systems that are fundamental to create pleasant and restful running experiences are identified for further investigation in future research.

In gesture-aided learning (GAL), learners perform specific body gestures while rehearsing the associated learning content. Although this form of embodiment has been shown to benefit learning outcomes, it has not yet been incorporated into e-learning. This work presents a generic system design for an online GAL platform. It is comprised of five modules for planning, administering, and monitoring remote GAL lessons. To validate the proposed design, a reference implementation for word learning was demonstrated in a field test. 19 participants independently took a predefined online GAL lesson and rated their experience on the System Usability Scale and a supplemental questionnaire. To monitor the correct gesture execution, the reference implementation recorded the participants’ webcam feeds and uploaded them to the instructor for review. The results from the field test show that the reference implementation is capable of delivering an e-learning experience with GAL elements. Designers of e-learning platforms may use the proposed design to include GAL in their applications. Beyond its original purpose in education, the platform is also useful to collect and annotate gesture data.