Sensory output produced by a robotic hand during grasping and manipulation evoked real-time nearly natural force and slippage feedback.

Currently, the typically adopted hand prosthesis surface electromyography (sEMG) control strategies do not provide the users with a natural control feeling and do not exploit all the potential of commercially available multi-fingered hand prostheses. Pattern recognition and machine learning techniques applied to sEMG can be effective for a natural control based on the residual muscles contraction of amputated people corresponding to phantom limb movements. As the researches has reached an advanced grade accuracy, these algorithms have been proved and the embedding is necessary for the realization of prosthetic devices. The aim of this work is to provide engineering tools and indications on how to choose the most suitable classifier, and its specific internal settings for an embedded control of multigrip hand prostheses. By means of an innovative statistical analysis, we compare 4 different classifiers: Nonlinear Logistic Regression, Multi-Layer Perceptron, Support Vector Machine and Linear Discriminant Analysis, which was considered as ground truth. Experimental tests have been performed on sEMG data collected from 30 people with trans-radial amputation, in which the algorithms were evaluated for both performance and computational burden, then the statistical analysis has been based on the Wilcoxon Signed-Rank test and statistical significance was considered at p < 0.05. The comparative analysis among NLR, MLP and SVM shows that, for either classification performance and for the number of classification parameters, SVM attains the highest values followed by MLP, and then by NLR. However, using as unique constraint to evaluate the maximum acceptable complexity of each classifier one of the typically available memory of a high performance microcontroller, the comparison pointed out that for people with trans-radial amputation the algorithm that produces the best compromise is NLR closely followed by MLP. This result was also confirmed by the comparison with LDA with time domain features, which provided not significant differences of performance and computational burden between NLR and LDA. The proposed analysis would provide innovative engineering tools and indications on how to choose the most suitable classifier based on the application and the desired results for prostheses control.

In the present study we introduced a simple test to assess the orientation error of an inertial sensors system for human movement analysis, both in static and dynamic conditions. In particular, the test was intended to quantify the sensitivity of the orientation error to direction and velocity of rotation. The test procedure was performed on a 5 MT9B sensors Xsens acquisition system, and revealed that the system orientation error, expressed by Euler angles decomposition, was sensitive both to direction and to velocity, being higher for fast movements: for mean rotation velocities of 180deg/s and 360deg/s, the worst case orientation error was 5.4deg and 11.6deg, respectively. The test can be suggested therefore as a useful tool to verify the user specific system accuracy without requiring any special equipment. In addition, the test provides further error information concerning direction and velocity of the movement which are not supplied by the producer, since they depend on the specific field of application

The aim of the present paper is to illustrate the phases of design and application of an innovative input source for an EMG upper limb prosthetic arm: a laryngophone, otherwise called throat microphone (t-mic). In the last years several different input sources were explored, from the implantable myoelectric sensors to the mechanomyographic sensors. The idea of controlling a prosthesis with vocal commands is quite recent but seems to be promising in helping users to better control their devices, improving the quality of their life.

Current state-of-the-art locomotion mode classifiers for controlling robotic lower-limb prostheses rely on multiple sensors to achieve high accuracy, prediction performance, and robustness to both speed changes and subject-specific gait patterns. However, multiple sensors placed on different body parts usually entail discomfort and poor usability for the user. This paper presents an intention detection method that relies on the features extracted from an inertial measurement unit worn on the thigh and an online phase estimator. The algorithm classifies the locomotion mode of the upcoming stride among the three modes of ground-level walking, stair ascent, and stair descent. A two-stage classification process first distinguishes between transient and steady-state strides and then classifies the locomotion mode of the impending stride based on directed acyclic graphs of binary classifiers. The classification is performed at 75% or 85% of the previous stride phase, respectively for steady-state and transient strides. Data were gathered from 10 healthy subjects and processed offline. Feature design and selection were based on the data of all subjects, while the classification performance was assessed by leave-one-subject-out cross-validation. Results presented a median recognition accuracy of 98.7% for steady-state strides and 95.6% for transitions, suggesting that the method was inherently robust to variations in gait cadence, since all of the features were phase-based and not dependent on fixed time intervals. These results inform the design of control strategies for active transfemoral prostheses able to predict the user’s locomotion intention during the next stride, using minimum sensors.

People with a high level of disability experience great difficulties to perform activities of daily living and resort to their residual motor functions in order to operate assistive devices. The commercially available interfaces used to control assistive manipulators are typically based on joysticks and can be used only by subjects with upper-limb residual mobilities. Many other solutions can be found in the literature, based on the use of multiple sensory systems for detecting the human motion intention and state. Some of them require a high cognitive workload for the user. Some others are more intuitive and easy to use but have not been widely investigated in terms of usability and user acceptance. The objective of this work is to propose an intuitive and robust user interface for assistive robots, not obtrusive for the user and easily adaptable for subjects with different levels of disability. The proposed user interface is based on the combination of M-IMU and EMG for the continuous control of an arm-hand robotic system by means of M-IMUs. The system has been experimentally validated and compared to a standard voice interface. Sixteen healthy subjects volunteered to participate in the study: 8 subjects used the combined M-IMU/EMG robot control, and 8 subjects used the voice control. The arm-hand robotic system made of the KUKA LWR 4+ and the IH2 Azzurra hand was controlled to accomplish the daily living task of drinking. Performance indices and evaluation scales were adopted to assess performance of the two interfaces.