Evaluation of Optimal Vibrotactile Feedback for Force-Controlled Upper Limb Myoelectric Prostheses.

The main goal of this study is to evaluate how to optimally select the best vibrotactile pattern to be used in a closed loop control of upper limb myoelectric prostheses as a feedback of the exerted force. To that end, we assessed both the selection of actuation patterns and the effects of the selection of frequency and amplitude parameters to discriminate between different feedback levels. A single vibrotactile actuator has been used to deliver the vibrations to subjects participating in the experiments. The results show no difference between pattern shapes in terms of feedback perception. Similarly, changes in amplitude level do not reflect significant improvement compared to changes in frequency. However, decreasing the number of feedback levels increases the accuracy of feedback perception and subject-specific variations are high for particular participants, showing that a fine-tuning of the parameters is necessary in a real-time application to upper limb prosthetics. In future works, the effects of training, location, and number of actuators will be assessed. This optimized selection will be tested in a real-time proportional myocontrol of a prosthetic hand.

A Deep Learning Approach to Estimate Multi-Level Mental Stress From EEG Using Serious Games.

Stress is revealed by the inability of individuals to cope with their environment, which is frequently evidenced by a failure to achieve their full potential in tasks or goals. This study aims to assess the feasibility of estimating the level of stress that the user is perceiving related to a specific task through an electroencephalograpic (EEG) system. This system is integrated with a Serious Game consisting of a multi-level stress driving tool, and Deep Learning (DL) neural networks are used for classification. The game involves controlling a vehicle to dodge obstacles, with the number of obstacles increasing based on complexity. Assuming that there is a direct correlation between the difficulty level of the game and the stress level of the user, a recurrent neural network (RNN) with a structure based on gated recurrent units (GRU) was used to classify the different levels of stress. The results show that the RNN model is able to predict stress levels above current state-of-the-art with up to 94% accuracy in some cases, suggesting that the use of EEG systems in combination with Serious Games and DL represents a promising technique in the prediction and classification of mental stress levels.

A Comparison of Myoelectric Control Modes for an Assistive Robotic Virtual Platform.

In this paper, we propose a daily living situation where objects in a kitchen can be grasped and stored in specific containers using a virtual robot arm operated by different myoelectric control modes. The main goal of this study is to prove the feasibility of providing virtual environments controlled through surface electromyography that can be used for the future training of people using prosthetics or with upper limb motor impairments. We propose that simple control algorithms can be a more natural and robust way to interact with prostheses and assistive robotics in general than complex multipurpose machine learning approaches. Additionally, we discuss the advantages and disadvantages of adding intelligence to the setup to automatically assist grasping activities. The results show very good performance across all participants who share similar opinions regarding the execution of each of the proposed control modes.

Multisensory Evaluation of Muscle Activity and Human Manipulability during Upper Limb Motor Tasks.

In this work, we evaluate the relationship between human manipulability indices obtained from motion sensing cameras and a variety of muscular factors extracted from surface electromyography (sEMG) signals from the upper limb during specific movements that include the shoulder, elbow and wrist joints. The results show specific links between upper limb movements and manipulability, revealing that extreme poses show less manipulability, i.e., when the arms are fully extended or fully flexed. However, there is not a clear correlation between the sEMG signals' average activity and manipulability factors, which suggests that muscular activity is, at least, only indirectly related to human pose singularities. A possible means to infer these correlations, if any, would be the use of advanced deep learning techniques. We also analyze a set of EMG metrics that give insights into how muscular effort is distributed during the exercises. This set of metrics could be used to obtain good indicators for the quantitative evaluation of sequences of movements according to the milestones of a rehabilitation therapy or to plan more ergonomic and bearable movement phases in a working task.

ARMIA: A Sensorized Arm Wearable for Motor Rehabilitation.

In this paper, we present ARMIA: a sensorized arm wearable that includes a combination of inertial and sEMG sensors to interact with serious games in telerehabilitation setups. This device reduces the cost of robotic assistance technologies to be affordable for end-users at home and at rehabilitation centers. Hardware and acquisition software specifications are described together with potential applications of ARMIA in real-life rehabilitation scenarios. A detailed comparison with similar medical technologies is provided, with a specific focus on wearable devices and virtual and augmented reality approaches. The potential advantages of the proposed device are also described showing that ARMIA could provide similar, if not better, the effectivity of physical therapy as well as giving the possibility of home-based rehabilitation.

[Endogenous ochronosis: a case description]. / Ocronosis endógena: descripción de un caso.

Endogenous ochronosis or alkaptonuria is an autosomal recessive disease caused by a deficiency of the enzyme homogentisic acid oxidase. Affected individuals excrete high levels of homogentisic acid in the urine, which darkens when it is alkalinized or oxidized. Deposits of blackish-brown pigment also occur in connective tissue; this causes, usually starting around the age of 40, the typical external manifestations of this disease, along with disorders in other organs. We present a clinical case of endogenous ochronosis, a very infrequent disease in our milieu. We will discuss the most noteworthy features of the case.

Ocronosis endógena: descripción de un caso / Endogenous ochronosis: a case description

La ocronosis endógena o alcaptonuria es una enfermedad autosómica recesiva producida por una insuficiencia de la enzima homogentísica oxidasa. Los individuos afectados excretan altas concentraciones de ácido homogentísico en orina que se oscurece cuando esta es alcalinizada u oxidada. También tiene lugar el depósito de pigmento marrón negruzco en el tejido conjuntivo, que provoca habitualmente, a partir de la cuarta década, las típicas manifestaciones externas de esta enfermedad junto con alteraciones en otros órganos. Presentamos un caso clínico de ocronosis endógena, proceso muy poco frecuente en nuestro medio y detallaremos los aspectos más destacados del caso

Endogenous ochronosis or alkaptonuria is an autosomal recessive disease caused by a deficiency of the enzyme homogentisic acid oxidase. Affected individuals excrete high levels of homogentisic acid in the urine, which darkens when it is alkalinized or oxidized. Deposits of blackish-brown pigment also occur in connective tissue; this causes, usually starting around the age of 40, the typical external manifestations of this disease, along with disorders in other organs. We present a clinical case of endogenous ochronosis, a very infrequent disease in our milieu. We will discuss the most noteworthy features of the case