The length of the standing long jump (SLJ) is widely recognized as an indicator of developmental motor competence or sports conditional performance. This work aims at defining a methodology to allow athletes/coaches to easily measure it using the inertial measurement units embedded on a smartphone. A sample group of 114 trained young participants was recruited and asked to perform the instrumented SLJ task. A set of features was identified based on biomechanical knowledge, then Lasso regression allowed the identification of a subset of predictors of the SLJ length that was used as input of different optimized machine learning architectures. Results obtained from the use of the proposed configuration allow an estimate of the SLJ length with a Gaussian Process Regression model with a RMSE of 0.122 m in the test phase, Kendall’s τ < 0.1. The proposed models give homoscedastic results, meaning that the error of the models does not depend on the estimated quantity. This study proved the feasibility of using low-cost smartphone sensors to provide an automatic and objective estimate of SLJ performance in ecological settings.

Idiopathic toe walking (ITW) is a gait deviation characterized by forefoot contact with the ground and excessive ankle plantarflexion over the entire gait cycle observed in otherwise-typical developing children. The clinical evaluation of ITW is usually performed using optoelectronic systems analyzing the sagittal component of ankle kinematics and kinetics. However, in standardized laboratory contexts, these children can adopt a typical walking pattern instead of a toe walk, thus hindering the laboratory-based clinical evaluation. With these premises, measuring gait in a more ecological environment may be crucial in this population. As a first step towards adopting wearable clinical protocols embedding magneto-inertial sensors and pressure insoles, this study analyzed the performance of three algorithms for gait events identification based on shank and/or foot sensors. Foot strike and foot off were estimated from gait measurements taken from children with ITW walking barefoot and while wearing a foot orthosis. Although no single algorithm stands out as best from all perspectives, preferable algorithms were devised for event identification, temporal parameters estimate and heel and forefoot rocker identification, depending on the barefoot/shoed condition. Errors more often led to an erroneous characterization of the heel rocker, especially in shoed condition. The ITW gait specificity may cause errors in the identification of the foot strike which, in turn, influences the characterization of the heel rocker and, therefore, of the pathologic ITW behavior.

Advances in wearable sensing technology are driving a new era of personalized health monitoring. In contrast to the hard, rigid form factors of conventional wearable sensors, these emerging skin-interfaced systems support high-quality physiological measurements across biophysical, biochemical, and kinematic signals of interest. These platforms enable continuous monitoring of complex physiological processes with unprecedented detail as a result of a seamless, conformal skin interface and advanced wireless communications capabilities. These platforms integrate flexible materials, miniaturized electronics, and wireless communication to provide detailed physiological data during daily activities. This review examines how skin-interfaced wearables are advancing patient care, remote monitoring, and large-scale health studies. We highlight critical barriers to clinical adoption including interpreting data, validating devices, and integration into health care systems. Key opportunities include sustainable manufacturing, point-of-care fabrication, and development of disease-specific digital biomarkers. By addressing these challenges through collaboration among engineers, clinicians, and data scientists, wearable sensors can expand patient access to advanced physiological monitoring and transform personalized medicine.

As the mental health of college students continues to worsen, identifying students who need additional support and timely, targeted intervention is vital for college campuses. Leveraging Oura rings, a validated consumer wearable, we identify and examine heart rate variability (HRV) trajectories in 233 first-semester college students to characterize patterns of physiological well-being. The dominant trajectory among students was a decline in HRV (43.25%), followed by students who fluctuated but ultimately improved their HRV (24.46%), and students who remained consistent around their baseline, but experienced low points at the end of October and the end of the semester (19.3%). The least common HRV trajectory was a continuous improvement across the semester (12.9%). Findings suggest strong associations between HRV trajectories and experiential backgrounds, including first-generation status and total reported adverse life experiences, as well as weekly self-reported stress levels, highlighting the potential of wearables for providing real-time insight that could enable targeted intervention for student well-being.

Multiple sclerosis (MS) is a neurodegenerative disease that affects sensorimotor function, particularly in the lower limbs, leading to increased fatigue and impaired mobility. The 30-second chair stand test (30CST) is a functional assessment that captures muscular strength and endurance in people with MS (PwMS). This study introduces Rotational Power (RP), which is a new metric derived from a single thigh-worn accelerometer, that can serve as a surrogate for body-mass normalized mechanical power during sit-to-stand (SI-ST) and stand-to-sit (ST-SI) transitions. We validate the metric both analytically and clinically in seventeen PwMS who performed a 30CST while instrumented with an accelerometer and a motion-capture system for reference. Analytical validation demonstrated a strong correlation with peak mechanical power for both SI-ST (r = 0.79) and ST-SI (r = 0.60). Clinical validation revealed strong-to-moderate significant associations between the 95^th percentile of peak RP computed across the 30CST and established clinical measures, including the number of repetitions, activity specific balance confidence scale, and modified fatigue impact scale. The same analysis performed on the motion-capture mechanical power showed similar results and concordance in correlation direction. Analysis of acute fatigue during the 30CST showed a significant decline in RP during the task, particularly for concentric transitions. Findings suggest that RP is a valid and practical metric for monitoring functional performance and acute fatigue in PwMS using a single wearable sensor, calling for its future use in free-living settings.