The use of contemporary technology is widely recognised as a key tool for enhancing competitive performance in swimming. Video analysis is traditionally used by coaches to acquire reliable biomechanical data about swimming performance; however, this approach requires a huge computational effort, thus introducing a delay in providing quantitative information. Inertial and magnetic sensors, including accelerometers, gyroscopes and magnetometers, have been recently introduced to assess the biomechanics of swimming performance. Research in this field has attracted a great deal of interest in the last decade due to the gradual improvement of the performance of sensors and the decreasing cost of miniaturised wearable devices. With the aim of describing the state of the art of current developments in this area, a systematic review of the existing methods was performed using the following databases: PubMed, ISI Web of Knowledge, IEEE Xplore, Google Scholar, Scopus and Science Direct. Twenty-seven articles published in indexed journals and conference proceedings, focusing on the biomechanical analysis of swimming by means of inertial sensors were reviewed. The articles were categorised according to sensor's specification, anatomical sites where the sensors were attached, experimental design and applications for the analysis of swimming performance. Results indicate that inertial sensors are reliable tools for swimming biomechanical analyses.

The analysis of the joint kinematics during swimming plays a fundamental role both in sports conditioning and in clinical contexts. Contrary to the traditional video analysis, wearable inertial-magnetic measurements units (IMMUs) allow to analyse both the underwater and aerial phases of the swimming stroke over the whole length of the swimming pool. Furthermore, the rapid calibration and short data processing required by IMMUs provide coaches and athletes with an immediate feedback on swimming kinematics during training. This study aimed to develop a protocol to assess the three-dimensional kinematics of the upper limbs during swimming using IMMUs. Kinematics were evaluated during simulated dry-land swimming trials performed in the laboratory by eight swimmers. A stereo-photogrammetric system was used as the gold standard. The results showed high coefficient of multiple correlation (CMC) values, with median (first–third quartile) of 0.97 (0.93–0.95) and 0.99 (0.97–0.99) for simulated front-crawl and breaststroke, respectively. Furthermore, the joint angles were estimated with an accuracy increasing from distal to proximal joints, with wrist indices showing median CMC values always higher than 0.90. The present findings represent an important step towards the practical use of technology based on IMMUs for the kinematic analysis of swimming in applied contexts.

Human gait analysis can provide an excellent source for identifying and predicting pathologies and injuries. In this respect, sensorized insoles also have a great potential for extracting gait information. This, combined with mathematical techniques based on machine learning (ML), can potentialize biomechanical analyses. The present study proposes a proof-of-concept of a system based on vertical ground reaction force (vGRF) acquisition with a sensorized insole that uses an ML algorithm to identify different patterns of vGRF and extract biomechanical characteristics that can help during clinical evaluation. The acquired data from the system was clustered by an immunological algorithm (IA) based on vGRF during gait. These clusters underwent a data mining process using the classification and regression tree algorithm (CART), where the main characteristics of each group were extracted, and some rules for gait classification were created. As a result, the system proposed was able to collect and process the biomechanical behavior of gait. After the application of IA and CART algorithms, six groups were found. The characteristics of each of these groups were extracted and verified the capability of the system to collect and process the biomechanical behavior of gait, offering verification points that can help focus during a clinical evaluation.