With mechanical loading as the main risk factor for LBP in mind, exoskeletons are designed to reduce the load on the back by taking over a part of the required moment. The present study assessed the effect of a passive exoskeleton on back and abdominal muscle activation, hip and lumbar flexion and on the contribution of both the human and the exoskeleton to the L5/S1 net moment, during static bending at five different hand heights. Two configurations of the exoskeleton (LOW & HIGH) differing in angle-torque characteristics were tested. L5/S1 moments generated by the subjects were significantly reduced (15-20% for the most effective type) at all hand heights. LOW generated 4-11 Nm more support than HIGH at 50%, 25% and 0% upright stance hand height and HIGH generated 4-5 Nm more support than LOW at 100% and 75%. Significant reductions (11-57%) in back muscle activity were found compared to WITHOUT for both exoskeletons for some conditions. However, EMG reductions compared to WITHOUT were highly variable across subjects and not always significant. The device allowed for substantial lumbar bending (up to 70°) so that a number of participants showed the flexion-relaxation phenomenon, which prevented further reduction of back EMG by the device and even an increase from 2% to 6% MVC in abdominal activity at 25% hand height. These results indicate that flexion relaxation and its interindividual variation should be considered in future exoskeleton developments.

IntroductionInertial measurement units (IMUs) positioned on various body locations allow detailed gait analysis even under unconstrained conditions. From a medical perspective, the assessment of vulnerable populations is of particular relevance, especially in the daily-life environment. Gait analysis algorithms need thorough validation, as many chronic diseases show specific and even unique gait patterns. The aim of this study was therefore to validate an acceleration-based step detection algorithm for patients with Parkinson’s disease (PD) and older adults in both a lab-based and home-like environment.MethodsIn this prospective observational study, data were captured from a single 6-degrees of freedom IMU (APDM) (3DOF accelerometer and 3DOF gyroscope) worn on the lower back. Detection of heel strike (HS) and toe off (TO) on a treadmill was validated against an optoelectronic system (Vicon) (11 PD patients and 12 older adults). A second independent validation study in the home-like environment was performed against video observation (20 PD patients and 12 older adults) and included step counting during turning and non-turning, defined with a previously published algorithm.ResultsA continuous wavelet transform (cwt)-based algorithm was developed for step detection with very high agreement with the optoelectronic system. HS detection in PD patients/older adults, respectively, reached 99/99% accuracy. Similar results were obtained for TO (99/100%). In HS detection, Bland–Altman plots showed a mean difference of 0.002 s [95% confidence interval (CI) −0.09 to 0.10] between the algorithm and the optoelectronic system. The Bland–Altman plot for TO detection showed mean differences of 0.00 s (95% CI −0.12 to 0.12). In the home-like assessment, the algorithm for detection of occurrence of steps during turning reached 90% (PD patients)/90% (older adults) sensitivity, 83/88% specificity, and 88/89% accuracy. The detection of steps during non-turning phases reached 91/91% sensitivity, 90/90% specificity, and 91/91% accuracy.ConclusionThis cwt-based algorithm for step detection measured at the lower back is in high agreement with the optoelectronic system in both PD patients and older adults. This approach and algorithm thus could provide a valuable tool for future research on home-based gait analysis in these vulnerable cohorts.

van Dieën JH, van Leeuwen M, Faber GS. Learning to balance on one leg: motor strategy and sensory weighting. J Neurophysiol 114: 2967-2982. First published September 23, 2015 doi:10.1152/jn.00434.2015.-We investigated motor and sensory changes underlying learning of a balance task. Fourteen participants practiced balancing on one leg on a board that could freely rotate in the frontal plane. They performed six, 16-s trials standing on one leg on a stable surface (2 trials without manipulation, 2 with vestibular, and 2 with visual stimulation) and six trials on the balance board before and after a 30-min training. Center of mass (COM) movement, segment, and total angular momenta and board angles were determined. Trials on stable surface were compared with trials after training to assess effects of surface conditions. Trials pretraining and posttraining were compared to assess rapid (between trials pretraining) and slower (before and after training) learning, and sensory manipulation trials were compared with unperturbed trials to assess sensory weighting. COM excursions were larger on the unstable surface but decreased with practice, with the largest improvement over the pretraining trials. Changes in angular momentum contributed more to COM acceleration on the balance board, but with practice this decreased. Visual stimulation increased sway similarly in both surface conditions, while vestibular stimulation increased sway less on the balance board. With practice, the effects of visual and vestibular stimulation increased rapidly. Initially, oscillations of the balance board occurred at 3.5 Hz, which decreased with practice. The initial decrease in sway with practice was associated with upweighting of visual information, while later changes were associated with suppression of oscillations that we suggest are due to too high proprioceptive feedback gains.

Recently, two methods for quantifying the stability of a dynamical system have been applied to human locomotion: local stability (quantified by finite time maximum Lyapunov exponents, λs and λL) and orbital stability (quantified by maximum Floquet multipliers, MaxFm). In most studies published to date, data from optoelectronic measurement systems were used to calculate these measures. However, using wireless inertial sensors may be more practical as they are easier to use, also in ambulatory applications. While inertial sensors have been employed in some studies, it is unknown whether they lead to similar stability estimates as obtained with optoelectronic measurement systems. In the present study, we compared stability measures of human walking estimated from an optoelectronic measurement system with those calculated from an inertial sensor measurement system. Subjects walked on a treadmill at three different speeds while kinematics were recorded using both measurement systems. From the angular velocities and linear accelerations, λs, λL, and MaxFm were calculated. Both measurement systems showed the same effects of walking speed for all variables. Estimates from both measurement systems correlated high for λs and λL, (R > 0.85) but less strongly for MaxFm (R = 0.66). These results indicate that inertial sensors constitute a valid alternative for an optoelectronic measurement system when assessing dynamic stability in human locomotion, and may thus be used instead, which paves the way to studying gait stability during natural, everyday walking.

a b s t r a c tInertial motion capture (IMC) systems have become increasingly popular for ambulatory movement analysis. However, few studies have attempted to use these measurement techniques to estimate kinetic variables, such as joint moments and ground reaction forces (GRFs).Therefore, we investigated the performance of a full-body ambulatory IMC system in estimating 3D L5/S1 moments and GRFs during symmetric, asymmetric and fast trunk bending, performed by nine male participants. Using an ambulatory IMC system (Xsens/MVN), L5/S1 moments were estimated based on the upper-body segment kinematics using a top-down inverse dynamics analysis, and GRFs were estimated based on full-body segment accelerations.As a reference, a laboratory measurement system was utilized: GRFs were measured with Kistler force plates (FPs), and L5/S1 moments were calculated using a bottom-up inverse dynamics model based on FP data and lower-body kinematics measured with an optical motion capture system (OMC). Correspondence between the OMCþ FP and IMC systems was quantified by calculating root-mean-square errors (RMSerrors) of moment/force time series and the interclass correlation (ICC) of the absolute peak moments/forces.Averaged over subjects, L5/S1 moment RMSerrors remained below 10 Nm (about 5% of the peak extension moment) and 3D GRF RMSerrors remained below 20 N (about 2% of the peak vertical force). ICCs were high for the peak L5/S1 extension moment (0.971) and vertical GRF (0.998). Due to lower amplitudes, smaller ICCs were found for the peak asymmetric L5/S1 moments (0.690-0.781) and horizontal GRFs (0.559-0.948).In conclusion, close correspondence was found between the ambulatory IMC-based and laboratory-based estimates of back load.