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Six main areas of application are analysed: gait analysis, stabilometry, instrumented clinical tests, upper body mobility assessment, daily-life activity monitoring and tremor assessment. Each area is analyzed both from a methodological and applicative point of view. The focus on the methodological approaches is meant to provide an idea of the computational complexity behind a variable/parameter/index of interest so that the reader is aware of the reliability of the approach. The focus on the application is meant to provide a practical guide for advising clinicians on how inertial sensors can help them in their clinical practice. Expert commentary: Less expensive and more easy to use than other systems used in human movement analysis, wearable sensors have evolved to the point that they can be considered ready for being part of routine clinical routine.

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The aim of this study was to validate a wearable inertial measurement unit (IMU), containing a 3D accelerometer and gyroscope, for the estimation of countermovement jump height. The absolute vertical acceleration of the IMU positioned on the back of the participant at L5 level, compensated for trunk rotations, was used to obtain jump height by applying the equation of free-fall to the motion of the IMU. The methodology was tested on 28 participants performing five countermovement jumps each. A reference value for this quantity was obtained using stereophotogrammetry (35.4 cm, s = 4.9). Jump height scores obtained using the proposed methodology (35.9 cm, s = 5.5) presented no significant difference with respect to stereophotogrammetry (P = 0.61). A low bias of 0.6 cm confirmed the accuracy of the estimate, which also showed a high (r = 0.87) and significant (P < 0.0001) correlation with reference values. Furthermore, without compensating accelerations for trunk rotation, jump height was largely underestimated (P < 0.0001) (bias: -12.7 cm) and poorly associated (r = 0.31) with stereophotogrammetry. The results of this study show that the estimation of jump height using inertial sensors leads to accurate results when the measured accelerations are corrected for trunk rotations.

The purpose of this study was to identify consistent features in the signals supplied by a single inertial measurement unit (IMU), or thereof derived, for the identification of foot-strike and foot-off instants of time and for the estimation of stance and stride duration during the maintenance phase of sprint running. Maximal sprint runs were performed on tartan tracks by five amateur and six elite athletes, and durations derived from the IMU data were validated using force platforms and a high-speed video camera, respectively, for the two groups. The IMU was positioned on the lower back trunk (L1 level) of each athlete. The magnitudes of the acceleration and angular velocity vectors measured by the IMU, as well as their wavelet-mediated first and second derivatives were computed, and features related to foot-strike and foot-off events sought. No consistent features were found on the acceleration signal or on its first and second derivatives. Conversely, the foot-strike and foot-off events could be identified from features exhibited by the second derivative of the angular velocity magnitude. An average absolute difference of 0.005 s was found between IMU and reference estimates, for both stance and stride duration and for both amateur and elite athletes. The 95% limits of agreement of this difference were less than 0.025 s. The results proved that a single, trunk-mounted IMU is suitable to estimate stance and stride duration during sprint running, providing the opportunity to collect information in the field, without constraining or limiting athletes' and coaches' activities.

These findings suggest the use of the proposed methodology as a valid alternative to other indirect approaches for 1RM prediction. The mathematical construct is simply based on the definition of the 1RM, and it is fed with subject's muscle strength capacities measured during a specific exercise. Its reliability is, thus, expected to be not affected by those factors that typically jeopardize regression-based approaches.

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