An ambulatory monitoring system is developed for the estimation of spatio-temporal gait parameters. The inertial measurement unit embedded in the system is composed of one biaxial accelerometer and one rate gyroscope, and it reconstructs the sagittal trajectory of a sensed point on the instep of the foot. A gait phase segmentation procedure is devised to determine temporal gait parameters, including stride time and relative stance; the procedure allows to define the time intervals needed for carrying an efficient implementation of the strapdown integration, which allows to estimate stride length, walking speed, and incline. The measurement accuracy of walking speed and inclines assessments is evaluated by experiments carried on adult healthy subjects walking on a motorized treadmill. Root-mean-square errors less than 0.18 km/h (speed) and 1.52% (incline) are obtained for tested speeds and inclines varying in the intervals [3, 6] km/h and [-5, + 15]%, respectively. Based on the results of these experiments, it is concluded that foot inertial sensing is a promising tool for the reliable identification of subsequent gait cycles and the accurate assessment of walking speed and incline.

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BackgroundPrevious studies have shown that a cerebrovascular accident disrupts the coordinated control of leg muscles during locomotion inducing asymmetric gait patterns. However, the ability of muscle synergies and spinal maps to reflect the redistribution of the workload between legs after the trauma has not been investigated so far.MethodsTo investigate this issue, twelve post-stroke and ten healthy participants were asked to walk on a treadmill at controlled speeds (0.5, 0.7, 0.9, 1.1 km/h), while the EMG activity of twelve leg muscles was recorded on both legs. The synergies underlying muscle activation and the estimated motoneuronal activity in the lumbosacral enlargement (L2-S2) were computed and compared between groups.ResultsResults showed that muscle synergies in the unaffected limb were significantly more comparable to those of the healthy control group than the ones in the affected side. Spinal maps were dissimilar between the affected and unaffected sides highlighting a significant shift of the foci of the activity toward the upper levels of the spinal cord in the unaffected leg.ConclusionsMuscle synergies and spinal maps reflect the asymmetry as a motor deficit after stroke. However, further investigations are required to support or reject the hypothesis that the altered muscular organization highlighted by muscle synergies and spinal maps may be due to the concomitant contribution of the altered information coming from the upper part of the CNS, as resulting from the stroke, and to the abnormal sensory feedback due to the neuromuscular adaptation of the patients.

Electromyographic (EMG) signals can represent an interesting solution to control artificial hands because they are easy to record and can allow the user to control different robotic systems. However, after limb amputation the 'homologous' muscles are no more available to control the prosthetic device and for this reason complex pattern recognition approaches have to be developed to extract the voluntary commands by the user. This makes the control strategy less natural and acceptable and asks for alternative approaches. At the same time, it has been recently shown that (in monkeys) it is possible to discriminate grasping tasks just analyzing the activation onset/offset of upper limb muscles during the reaching phase. This kind of information can be very interesting because it can allow the development of a natural EMG-based control strategy based on the natural muscular activities selected by the central nervous system. In this paper, preliminary experiments have been carried out in order to verify whether these results can be confirmed also in human beings. In particular, a support vector machine (SVM) based pattern recognition algorithm has been developed and used for the prediction of grip types from the EMG recorded from proximal and distal muscles during reach to grasp movements of three able bodied subjects.

Reach-to-grasp tasks are composed of several actions that are more and more considered as simultaneously controlled by the central nervous system in a feedforward manner (at least for well-known activities). If this hypothesis is correct, during prehension tasks, the activity of proximal muscles (and not only of the distal ones used to control finger movements) is modulated according to the kind of object to be grasped and its position. This means that different objects could be identified by processing the electromyographic (EMG) signals recorded from proximal muscles. In this paper, specific experiments have been carried out to support this hypothesis in able-bodied subjects. The results achieved seem to confirm this possibility by showing that the activation of proximal muscles can be statistically different for different grip types. This finding supports the hypothesis that proximal and distal muscles are simultaneously controlled during reaching and grasping. Moreover, this kind of information could allow the development of an EMG-based control strategy based on the natural muscular activities selected by the central nervous system.