We present a low-frequency stimulation method via multi-pad electrodes for delaying muscle fatigue. We compared two protocols for muscle activation of the quadriceps in paraplegics. One protocol involved a large cathode at 30 HZ (HPR, high pulse-rate), and the other involved four smaller cathodes at 16 HZ (LPR, low pulse-rate). The treatment included 30-min daily sessions for 20 days. One leg was treated with the HPR protocol and the other with the LPR protocol. Knee-joint torque was measured before and after therapy to assess the time interval before the knee-joint torque decreased to 70% of the initial value. The HPR therapy provided greater increases in muscle endurance and force in prolonged training. Yet the LPR stimulation produced less muscle fatigue compared to the HPR stimulation. The results suggest that HPR is the favored protocol for training, and LPR is better suited for prolonged stimulation.

BackgroundFunctional electrical stimulation (FES) applied via transcutaneous electrodes is a common rehabilitation technique for assisting grasp in patients with central nervous system lesions. To improve the stimulation effectiveness of conventional FES, we introduce multi-pad electrodes and a new stimulation paradigm.MethodsThe new FES system comprises an electrode composed of small pads that can be activated individually. This electrode allows the targeting of motoneurons that activate synergistic muscles and produce a functional movement. The new stimulation paradigm allows asynchronous activation of motoneurons and provides controlled spatial distribution of the electrical charge that is delivered to the motoneurons. We developed an automated technique for the determination of the preferred electrode based on a cost function that considers the required movement of the fingers and the stabilization of the wrist joint. The data used within the cost function come from a sensorized garment that is easy to implement and does not require calibration. The design of the system also includes the possibility for fine-tuning and adaptation with a manually controllable interface.ResultsThe device was tested on three stroke patients. The results show that the multi-pad electrodes provide the desired level of selectivity and can be used for generating a functional grasp. The results also show that the procedure, when performed on a specific user, results in the preferred electrode configuration characteristics for that patient. The findings from this study are of importance for the application of transcutaneous stimulation in the clinical and home environments.

Combining the performance of multipad electrodes (increased selectivity and facilitated positioning) with sDLFAS (decreased fatigue) can improve many FES applications in both the lower and upper extremities.

We hypothesize that the asynchronous low frequency stimulation of pads within multi-pad electrode will be less fatiguing compared to the conventional stimulation (two single pad electrodes) when generating comparable large forces of paralyzed human muscles. The experiments to verify the hypothesis were conducted on quadriceps of six individuals with chronic spinal cord injury (ASIA score A) who had not participated in any electrical stimulation program. The following stimulation protocols were compared: stimulation with a self adhesive 7 cm x 10 cm Pals Platinum cathode positioned over the top of the quadriceps (f = 40 Hz), and four oval 4 cm x 6 cm cathodes positioned over the proximal upper leg (f = 16 Hz). The anode in both cases was the 7 cm x 10 cm Pals Platinum electrode positioned over the distal part of the quadriceps. We measured the knee joint torque vs. time with a custom made apparatus, and estimated the interval before the knee joint torque decreased to 70% of the maximum. Mean fatigue interval increase for the four-pad stimulation protocol vs. single-pad stimulation protocol was 153.18%. This suggests that the use of multi-pad electrodes is favorable in cases where a prolonged stimulation of muscles is required.

Reaching and grasping impairments significantly affect the quality of life for people who have experienced a stroke or spinal cord injury. The long-term well-being of patients varies greatly according to the restorable residual capabilities. Electrical stimulation could be a promising solution to restore motor functions in these conditions, but its use is not clinically widespread. Here, we introduce the HandNMES, an electrode array (EA) for neuromuscular electrical stimulation (NMES) aimed at grasp training and assistance. The device was designed to deliver electrical stimulation to extrinsic and intrinsic hand muscles. Six independent EAs, positioned on the user forearm and hand, deliver NMES pulses originating from an external stimulator equipped with demultiplexers for interfacing with a large number of electrodes. The garment was designed to be adaptable to user needs and anthropometric characteristics; size, shape, and contact materials can be customized, and stimulation characteristics such as intensity of stimulation and virtual electrode location, and size can be adjusted. We performed extensive tests with nine healthy subjects showing the efficacy of the HandNMES in terms of stimulation performance and personalization. Because encouraging results were achieved, in the coming months, the HandNMES device will be tested in pilot clinical trials.

The measurement of human vital signs is a highly important task in a variety of environments and applications. Most notably, the electrocardiogram (ECG) is a versatile signal that could indicate various physical and psychological conditions, from signs of life to complex mental states. The measurement of the ECG relies on electrodes attached to the skin to acquire the electrical activity of the heart, which imposes certain limitations. Recently, due to the advancement of wireless technology, it has become possible to pick up heart activity in a contactless manner. Among the possible ways to wirelessly obtain information related to heart activity, methods based on mm-wave radars proved to be the most accurate in detecting the small mechanical oscillations of the human chest resulting from heartbeats. In this paper, we presented a method based on a continuous-wave Doppler radar coupled with an artificial neural network (ANN) to detect heartbeats as individual events. To keep the method computationally simple, the ANN took the raw radar signal as input, while the output was minimally processed, ensuring low latency operation (<1 s). The performance of the proposed method was evaluated with respect to an ECG reference (“ground truth”) in an experiment involving 21 healthy volunteers, who were sitting on a cushioned seat and were refrained from making excessive body movements. The results indicated that the presented approach is viable for the fast detection of individual heartbeats without heavy signal preprocessing.

In contemporary muscle-computer interfaces for upper limb prosthetics there is often a trade-off between control robustness and range of executable movements. As a very low movement error rate is necessary in practical applications, this often results in a quite severe limitation of controllability; a problem growing ever more salient as the mechanical sophistication of multifunctional myoelectric prostheses continues to improve. A possible remedy for this could come from the use of multi-label machine learning methods, where complex movements can be expressed as the superposition of several simpler movements. Here, we investigate this claim by applying a multi-labeled classification scheme in the form of a deep convolutional neural network (CNN) to high density surface electromyography (HD-sEMG) recordings. We use 16 independent labels to model the movements of the hand and forearm state, representing its major degrees of freedom. By training the neural network on 16 × 8 sEMG image sequences 24 samples long with a sampling rate of 2048 Hz to detect these labels, we achieved a mean exact match rate of 78.7% and a mean Hamming loss of 2.9% across 14 healthy test subjects. With this, we demonstrate the feasibility of highly versatile and responsive sEMG control interfaces without loss of accuracy.

The various existing measures to quantify upper limb use from wrist-worn inertial measurement units can be grouped into three categories: 1) Thresholded activity counting, 2) Gross movement score and 3) machine learning. However, there is currently no direct comparison of all these measures on a single dataset. While machine learning is a promising approach to detecting upper limb use, there is currently no knowledge of the information used by machine learning measures and the data-related factors that influence their performance. The current study conducted a direct comparison of the 1) thresholded activity counting measures, 2) gross movement score,3) a hybrid activity counting and gross movement score measure (introduced in this study), and 4) machine learning measures for detecting upper-limb use, using previously collected data. Two additional analyses were also performed to understand the nature of the information used by machine learning measures and the influence of data on the performance of machine learning measures. The intra-subject random forest machine learning measure detected upper limb use more accurately than all other measures, confirming previous observations in the literature. Among the non-machine learning (or traditional) algorithms, the hybrid activity counting and gross movement score measure performed better than the other measures. Further analysis of the random forest measure revealed that this measure used information about the forearm’s orientation and amount of movement to detect upper limb use. The performance of machine learning measures was influenced by the types of movements and the proportion of functional data in the training/testing datasets. The study outcomes show that machine learning measures perform better than traditional measures and shed some light on how these methods detect upper-limb use. However, in the absence of annotated data for training machine learning measures, the hybrid activity counting and gross movement score measure presents a reasonable alternative. We believe this paper presents a step towards understanding and optimizing measures for upper limb use assessment using wearable sensors.