In the prosthetics field, one of the most important bottlenecks is still the human-machine interface, namely the socket. Indeed, several amputees still reject their prostheses or report a low satisfaction level, due to factors dealing with the socket-tissues interaction. The aim of this paper is to describe the main parameters (displacements, stress, volume fluctuations and temperature) that affect the stump-socket interface and reduce the comfort of limb prostheses. In the review, a classification of the different socket types proposed in the literature is reported, together with an analysis of advantages and disadvantages of the different solutions, from multiple viewpoints. A specific focus concerns the technological solutions available commercially and in the state-of-the-art to face an altered distribution of stresses on the residual limb tissues, volume fluctuations affecting the stump overtime, and temperature variations affecting the residual tissues within the socket. The open challenges in this research field are highlighted and the possible future routes are discussed, towards the ambitious objective of achieving an advanced socket able to self-adapt in real-time to the complex interplay of factors affecting the stump, during both static and dynamic tasks.

A prosthetic hand with human-like grasping behavior outperforms what is required in the execution of activities of daily living.

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.

Although skeletal muscle repairs itself following small injuries, genetic diseases or severe damages may hamper its ability to do so. Induced pluripotent stem cells (iPSCs) can generate myogenic progenitors, but their use in combination with bioengineering strategies to modulate their phenotype has not been sufficiently investigated. This review highlights the potential of this combination aimed at pushing the boundaries of skeletal muscle tissue engineering. First, the overall organization and the key steps in the myogenic process occurring in vivo are described. Second, transgenic and non-transgenic approaches for the myogenic induction of human iPSCs are compared. Third, technologies to provide cells with biophysical stimuli, biomaterial cues, and biofabrication strategies are discussed in terms of recreating a biomimetic environment and thus helping to engineer a myogenic phenotype. The embryonic development process and the pro-myogenic role of the muscle-resident cell populations in co-cultures are also described, highlighting the possible clinical applications of iPSCs in the skeletal muscle tissue engineering field.