Search

The paper presents a novel method for the classification of gait phases for power gait orthosis users based on machine learning. The classification uses depth images collected from a Time of Flight camera embedded in the crutches employed for the assisted gait. The machine learning algorithm foresees an initial phase of data collection and processing, identifying the 3D points belonging to the foot and those belonging to the floor. From these, a feature set is computed analyzing the values of percentiles of distances of the foot from the floor, and passed to a modified version of Random Forest classifier, called Sigma-z Random Forest. The classifier considers the uncertainties associated to each feature set and provides both the classification of the gait phase (stance or swing) and an associated confidence value. In this work, we propose the use of the confidence value to improve the reliability of the gait phase classification, by applying an optimized threshold to the confidence value obtained for each new frame. The algorithm has been tested on different subjects and environments. An average classification accuracy of 87.3% has been obtained (+6.3% with respect to the standard random forest classifier), with a minor loss of unclassifiable frames. Results highlight that unclassifiable samples are usually associated to transitions between stance and swing. [ABSTRACT FROM AUTHOR]

The PMD Camboard Picoflexx Time-of-Flight (ToF) camera is evaluated against the Microsoft Kinect V2 to assess its performance in the context of markerless motion capture system for human body kinematics measurements. Various error sources such as the warm-up time, the depth distortion, the amplitude related error, the signal-to-noise ratio, and limitations such as their dependence on the illumination pattern and on the target distance, are studied and compared. The Picoflexx device is also compared to the Kinect V2 in relation to the quality of shape reconstructions, to assess its adequateness in modeling human body segments, and human body kinematics measurements. The final result of this paper is definitely useful to the research community, demonstrating that, even if the Picoflexx performs lower than the Kinect concerning the measurement performances, its behavior in estimating the volume of the body segments, the angles at the joints for human body kinematics, and the angle at the ankle in assisted walking applications is definitely satisfactory. These results are extremely significant to obtain accurate estimates of the parameters of the human body models in markerless motion capture applications, especially in laboratory-free environments, where compactness, lightweight, wireless connection, and low-power consumption are of outmost importance. [ABSTRACT FROM AUTHOR]

This paper describes the design, development, and characterization of two wireless instrumented crutches for gait monitoring in order to provide clinicians quantitative parameters of upper limbs’ contributions during walking. These parameters could be used to teach orthopedic patients to correctly use these supports and minimize problems connected to their usage. These instrumented crutches allow monitoring axial forces and shear forces, tilt angles, and time of impact on the ground in real time. Each crutch is composed of three strain-gauge bridges for measuring axial and shear forces, a conditioning circuit with transmission module, a triaxial accelerometer, a power management circuit, two batteries, and a biofeedback. The data are wirelessly transmitted via Bluetooth without needing any further readout unit, from the crutches to a personal computer, where the data are processed and displayed by a program created in LabVIEW. Each instrumented crutch was tested to assess the response of the accelerometer and the three strain-gauge bridges using a setup designed ad hoc. The mean experimental standard deviation was about 42 mV for axial forces corresponding to about 8 N and about 35 mV for shear forces corresponding to about 4 N. Hysteresis, linearity, and drift were calculated, and the obtained accuracy was about 8–9 N for axial forces and 4–5 N for shear forces. Furthermore, the crutches were tested during a walking activity of ten healthy subjects along a straight path for several trials. These crutches were used for a common analysis usually reported in the literature for weight bearing evaluation. The subjects were monitored performing a nonweight bearing (NWB) and a partial weight bearing (PWB) during a three-point gait. The results showed a mean of 102% ± 16% for NWB tests and a mean of 19% ±% for 10% PWB tests; these values are in agreement with similar studies in the literature. The simplicity that includes only constitutive strain gauges and a separable circuit board allows the achievement of the objectives of simplicity, ease of use, and noninvasiveness. Therefore, these crutches could be used as a support tool for controlling the use of crutches during walking not only in hospitals but also at home. [ABSTRACT FROM PUBLISHER]