Stroke patients can have spastic paresis of the lower leg, impeding an ankle which hinders gait. A novel orthosis has been developed which counteracts this impediment to the ankle. It is expected that gait training will improve stroke patients' use of the orthosis by increasing their ankle dynamics. Gait training with biofeedback, which is based on physiological signal, has been shown to be effective for stroke patients. The main design requirement for the biofeedback is that it facilitates learning of an increased active range of motion of the ankle. To fulfill this requirement the biofeedback is based on the maximum angle in plantar- and dorsiflexion during the swing phase of the impeded leg. In this research, the biofeedback is validated on healthy participants with an impeded right ankle performing five gait trials. First an unimpeded reference trial was conducted capturing normal gait. After which the participant's ankle was impeded. During the initial impeded trial the participant got accustomed to the impediment during gait. Then two trials with feedback were conducted followed by a retention trial without feedback. During the retention trial the effects the biofeedback has on the ankle dynamics are determined. The outcome measures were chosen to validate whether the biofeedback facilitated learning of an increased active range of motion of the ankle. The outcome measures were the increase of active range of motion of the ankle from the initial impeded trial to the retention trial and the error quotient. The error quotient is a measure showing to what extent the angles making up the active range of motion during a trial were the same as during the reference trial. The active range of motion of the ankle of participants increased (p < 0.001) from the initial impeded trial to the retention trial. Moreover a significant decrease in the error quotient of participants was found between the initial impeded trial and the first feedback trial (p = 0.033), second feedback trial (p = 0.013) and retention trial (p = 0.020). Therefore, the biofeedback facilitated learning of an increased active range of motion of the ankle to participants. Further research is required to determine how to best adapt the biofeedback such that it is suitable for use by stroke patients in daily life.

Purpose – The entropy algorithm is a recently developed statistic for quantifying the complexity of time series data. To date, research of biomechanics and motor control discussed whether entropy algorithms could be used as a convenient tool to identify healthy gait function, to evaluate outcomes of physical therapies and to monitor the progression of disease. Here, we show that Sample Entropy (SaEn) is a sensitive measure for exposing complexity changes in human gait function.

Methods – We analyzed signal complexity changes in electromyography (EMG), ground reaction force (GRF) and joint angle (GA) time series data of asymmetrical step tasks. We used the coarse-grained time series method and the SaEn algorithm, to determine the temporal resolution that contained most complex structures per datatype. Subsequently, we analyzed complexity changes with age and with walking velocity in the selected resolution. We analyzed complexity changes with age, since healthy gait function is known to deteriorate with age. In turn, we analyzed complexity changes with walking velocity, since walking velocity is known to alter gait function. Eighteen young (mean age 23.27 +/- 1.79 years) and nineteen old (mean age 66.37 +/- 5.26 years) subjects were analyzed for an equal number of strides, described by an equal number of samples, to account for the SaEn dataset length bias.

Results – Age increased entropy in EMG signals. Consecutively, age decreased GRF entropy in the medial-lateral (ML) component for short steps and increased entropy for long steps. Lastly, age decreased entropy in GA signals. Furthermore, walking velocity decreased entropy in EMG signals. Consecutively, walking velocity increased GRF entropy in anterior-posterior (AP) and vertical (VE) components and decreased entropy in the medial-lateral (ML) component. Lastly, walking velocity increased entropy in GA signals.

Conclusions – We portrayed that EMG, GRF and GA signals of human gait altered in entropy with walking velocity and with age. Therefore, our results demonstrate the feasibility of SaEn to quantify changes in healthy gait function. Additional research should confirm possible future clinical applications for entropy algorithms.

Muscles consists of multiple motor units (MUs) that produce the force required for our movements. Motor unitproperties are responsible for the relation between neural excitation and muscle force (excitation-force relation). Theforce of the muscle is dictated by the recruitment and the force output of the different motor units. The recruitmentis physiologically determined by the recruitment thresholds of the motor units, where motor units with a higher forceoutput are thought to have a higher recruitment threshold, conform Henneman’s orderly recruitment principle. Thegoal of this study was to predict the effects of the MU-threshold and MU-force output distributions on the excitation-force relation of a muscle. Assuming that a muscle’s excitation-force relation depends on the values of the motor unitproperties present in the muscle, the recruitment threshold and maximum force distributions among the motor unitscan be derived from muscle force recordings. To achieve the goal a computer model was build to simulate motor unitrecruitment and force production of a group of MUs with different (combinations) of MU-threshold and maximum MU-force output distributions. Both MU-threshold distribution and maximum MU-force output distribution of the musclewere mathematically varied between linear and exponential curves. Input of the muscle model was a linear increasingneural activation profile and the force output profile of the muscle was simulated. The resultant MU-activation andforce patterns were compared with published results of simultaneous MU-recruitment and force recordings obtainedfrom literature. The simulation results showed that the excitation-force profile does not show characteristics thatcan be linked to either the MU-threshold distribution or the MU-force output. As a consequence, force recordingsalone are not sufficient to estimate the recruitment threshold and maximum force distributions. However, the use of acomputer model in combination with additional recorded data such as (surface) electromygraphy (EMG) is expectedto show results from which estimating MU properties is possible.