RESUMEN
Recent studies on human upper limb motion highlighted the benefit of dimensionality reduction techniques to extrapolate informative joint patterns. These techniques can simplify the description of upper limb kinematics in physiological conditions, serving as a baseline for the objective assessment of movement alterations, or to be implemented in a robotic joint. However, the successful description of kinematic data requires a proper alignment of the acquisitions to correctly estimate kinematic patterns and their motion variability. Here, we propose a structured methodology to process and analyze upper limb kinematic data, considering time warping and task segmentation to register task execution on a common normalized completion time axis. Functional principal component analysis (fPCA) was used to extract patterns of motion of the wrist joint from the data collected by healthy participants performing activities of daily living. Our results suggest that wrist trajectories can be described as a linear combination of few functional principal components (fPCs). In fact, three fPCs explained more than 85% of the variance of any task. Wrist trajectories in the reaching phase of movement were highly correlated among participants and significantly more than trajectories in the manipulation phase ( [Formula: see text]). These findings may be useful in simplifying the control and design of robotic wrists, and could aid the development of therapies for the early detection of pathological conditions.
Asunto(s)
Actividades Cotidianas , Muñeca , Humanos , Muñeca/fisiología , Extremidad Superior/fisiología , Movimiento (Física) , Articulación de la Muñeca , Movimiento/fisiología , Fenómenos Biomecánicos , Rango del Movimiento Articular/fisiologíaRESUMEN
Archery technique requires a coordinated activation of shoulder girdle and upper extremity muscles to perform a successful shot. The analysis of muscle synergies can provide information about the motor strategy that underlies the shooting performance, also supporting the investigation of motor impairments in athletes with disability. For this purpose, electromyographic (EMG) data from five muscles were collected from a non-disabled and a W1 category Paralympic athlete, and muscle synergies were extracted from EMG envelopes using non-negative matrix factorization. Muscle synergies analysis revealed features of the motor strategy specific to the athletes' shooting technique, such as the contribution of the biceps muscle instead of the posterior deltoid during the arrow drawing and target aiming in the Paralympic athlete compared to the non-disabled athlete. It is concluded that the evaluation of the muscle synergies may be a valuable tool for exploring the motor strategies adopted by athletes with disability, providing useful information to improve athletic performance and possibly prevent the risk of injury.
Asunto(s)
Atletas , Extremidad Superior , Fenómenos Biomecánicos , Humanos , Músculo Esquelético , HombroRESUMEN
Human motion analysis is gaining increased importance in several fields, from movement assessment in rehabilitation to recreational applications such as virtual coaching. Among all the technologies involved in motion capture, Magneto-Inertial Measurements Units (MIMUs) is one of the most promising due to their small dimensions and low costs. Nevertheless, their usage is strongly limited by different error sources, among which magnetic disturbances, which are particularly problematic in indoor environments. Inertial Measurement Units (IMUs) could, thus, be considered as alternative solution. Indeed, relying exclusively on accelerometers and gyroscopes, they are insensitive to magnetic disturbances. Even if the literature has started to propose few algorithms that do not take into account magnetometer input, their application is limited to robotics and aviation. The aim of the present work is to introduce a magnetic-free quaternion based Extended Kalman filter for upper limb kinematic assessment in human motion (i.e., yoga). The algorithm was tested on five expert yoga trainers during the execution of the sun salutation sequence. Joint angle estimations were compared with the ones obtained from an optoelectronic reference system by evaluating the Mean Absolute Errors (MAEs) and Pearson's correlation coefficients. The achieved worst-case was 6.17°, while the best one was 2.65° for MAEs mean values. The accuracy of the algorithm was further confirmed by the high values of the Pearson's correlation coefficients (lowest mean value of 0.86).Clinical Relevance- The proposed work validated a magnetic free algorithm for kinematic reconstruction with inertial units. It could be used as a wearable solution to track human movements in indoor environments being insensitive to magnetic disturbances, and thus could be potentially used also for rehabilitation purposes.