In this paper, a self-contained procedure to estimate the vertical, medial-lateral, and anterior-posterior displacement of a single sacrum-worn inertial measurement unit (IMU) is presented, which can be related to the human body center of mass (CoM) displacement during treadmill walking through an adaptation of the sacral marker method. Further, a magnetometer-free custom sensor-fusion algorithm for orientation estimation is proposed alongside a practical alignment procedure to refer relative IMU orientation estimation to a ground-fixed reference frame. Twelve healthy subjects performed two trials of treadmill walking at 3, 4 and 5 km/h for 150 s, with a sacrum-worn IMU. Orientation and displacement estimations were then compared with those obtained from an optoelectronic measurement system. Roll, pitch and yaw angles showed root mean square errors (RMSE) lower than 2 deg for walking trials at 3, 4 and 5 km/h, with Pearson’s correlation coefficient higher than 0.90 for each angle. Displacement accuracy was evaluated in terms of peak-to-trough distances and RMSE. Mean errors resulted lower than 1 mm for each axis of interest and for each gait speed, with RMSE not higher than 2.5 mm. The proposed off-line algorithm can be used in low-budget and infrastructure-free environments, to achieve reliable CoM displacement estimation during cyclic activities such as treadmill walking.

Muscle onset detection plays a key role in applications ranging from clinical to assistive technology. The Teager-Kaiser energy operator (TKEO) is an acknowledged tool used in surface electromyography (sEMG) signal conditioning for improving the performances of many change-point detection methods. Here, a TKEO extended version (ETKEO) was used to investigate its effects, for different SNR ranges, among a series of well-assessed algorithms, including a threshold-based one (TP). An optimization procedure on synthetic signals for the selection of the operator structure was also developed. The detection errors between TKEO and ETKEO, performed on real sEMG signals with SNR≤8 dB, showed significant (p<0.05) overall improvements, not lower than 30%, when ETKEO was used. When compared with more robust techniques preconditioned by ETKEO as well, i.e. wavelet-, CUSUM-and profile likelihood maximization-based algorithms, the TP detector reached comparable performances for each SNR band, also for the lowest one. The results support the relevance of using ETKEO to improve onset analysis methods for a wide range of low SNR values, being particularly suitable for applications such as myoelectric motion intention detection. Moreover, the ETKEO adaptable structure suggests its use for other biological signals, presenting different characteristics with respect to sEMG signals.

The functional reach test (FRT) is widely used for assessing dynamic balance stability in elderly and pathological subjects. Force platforms (FP) represent a fundamental part of the instrumented FRT experimental setup, due to the central role of center-of-pressure (COP) displacement in FRT analysis. Recently, the Nintendo Wii Balance Board (NBB) has been suggested as a low-cost and reliable device for ground reaction force and COP measurement in poorly dynamic motor tasks. Therefore, this study aimed to compare NBB-COP data with those obtained from a laboratory-grade platform during FRT. Data from forty-eight healthy subjects were simultaneously acquired from both devices. FP- and NBB-COP trajectories showed a remarkable correlation in both directions (r>0.990) and low root-mean-square error values (1.14±0.88 mm and 0.55±0.28 mm for anterior-posterior and medial-lateral direction). Fixed biases between COP-based parameters did not exceed 2% of the FP outcomes, with high consistency throughout the present measurement range (ICC consistency always >0.950). Only the COP mean-velocity exhibited a tendency towards proportional errors, which can be adjusted by a calibration of NBB data. Findings of this study confirmed the NBB validity for COP measurement in a widely used motor task as the functional reach, supporting the feasibility of NBB in research scenarios.

Hemiplegia is a neurological disorder that is often detected in children with cerebral palsy. Although many studies have investigated muscular activity in hemiplegic legs, few EMG-based findings focused on unaffected limb. This study aimed to quantify the asymmetric behavior of lower-limb-muscle recruitment during walking in mild-hemiplegic children from surface-EMG and foot-floor contact features. sEMG signals from tibialis anterior (TA) and gastrocnemius lateralis and foot-floor contact data during walking were analyzed in 16 hemiplegic children classified as W1 according to Winter’ scale, and in 100 control children. Statistical gait analysis, a methodology achieving a statistical characterization of gait by averaging surface-EMG-based features, was performed. Results, achieved in hundreds of strides for each child, indicated that in the hemiplegic side with respect to the non-hemiplegic side, W1 children showed a statistically significant: decreased number of strides with normal foot-floor contact; decreased stance-phase length and initial-contact sub-phase; curtailed, less frequent TA activity in terminal swing and a lack of TA activity at heel-strike. The acknowledged impairment of anti-phase eccentric control of dorsiflexors was confirmed in the hemiplegic side, but not in the contralateral side. However, a modified foot-floor contact pattern is evinced also in the contralateral side, probably to make up for balance requirements.

The surface electromyography signal (sEMG) is widely used for gesture characterization; its reliability is strongly connected to the features extracted from sEMG recordings. This study aimed to investigate the use of two complexity measures, i.e., fuzzy entropy (FEn) and permutation entropy (PEn) for hand gesture characterization. Fourteen upper limb movements, sorted into three sets, were collected on ten subjects and the performances of FEn and PEn for gesture descriptions were analyzed for different computational parameters. FEn and PEn were able to properly cluster the expected numbers of gestures, but computational parameters were crucial for ensuring clusters’ separability and proper gesture characterization. FEn and PEn were also compared with other eighteen classical time and frequency domain features through the minimum redundancy maximum relevance algorithm and showed the best predictive importance scores in two gesture sets; they also had scores within the subset of the best five features in the remaining one. Further, the classification accuracies of four different feature sets presented remarkable increases when FEn and PEn are included as additional features. Outcomes support the use of FEn and PEn for hand gesture description when computational parameters are properly selected, and they could be useful in supporting the development of robotic arms and prostheses myoelectric control.