A Smart Watch with Embedded Sensors to Recognize Objects, Grasps and Forearm Gestures

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All-ConvNet: A lightweight all CNN for neuromuscular activity recognition using instantaneous high-density surface EMG images

Neuromuscular activity recognition using low-resolution instantaneous high-density surface electromyography (HD-sEMG) images present a great challenge. The recent result shows the high potentiality and hence opens up new avenues for the development of more fluid and natural muscle-computer interfaces. However, the existing approaches employed a very large deep ConvNet, which requires learning >5.63 million training parameters only during fine-tuning and pre-trained on a very large-scale labeled HD-sEMG training datasets, as a result, it makes high-end resource bounded and computationally expensive. To overcome this problem, we propose a lightweight All-ConvNet model that consists solely of convolutional layers, a simple yet efficient framework for learning instantaneous HD-sEMG images from scratch through random initialization. Without using any pre-trained models, our proposed lightweight All-ConvNet demonstrate very competitive or even state of the art performance on a current benchmarks HD-sEMG dataset, while requires learning only ~460k training parameters and using ~12xsmaller dataset. The experimental results proved that the proposed lightweight All-ConvNet is highly effective for learning discriminative features for low-resolution instantaneous HD-sEMG image recognition and low-latency processing especially in the data and high-end resource constrained scenarios

Hand Motion Classification Using a Multi-Channel Surface Electromyography Sensor

The human hand has multiple degrees of freedom (DOF) for achieving high-dexterity motions. Identifying and replicating human hand motions are necessary to perform precise and delicate operations in many applications, such as haptic applications. Surface electromyography (sEMG) sensors are a low-cost method for identifying hand motions, in addition to the conventional methods that use data gloves and vision detection. The identification of multiple hand motions is challenging because the error rate typically increases significantly with the addition of more hand motions. Thus, the current study proposes two new methods for feature extraction to solve the problem above. The first method is the extraction of the energy ratio features in the time-domain, which are robust and invariant to motion forces and speeds for the same gesture. The second method is the extraction of the concordance correlation features that describe the relationship between every two channels of the multi-channel sEMG sensor system. The concordance correlation features of a multi-channel sEMG sensor system were shown to provide a vast amount of useful information for identification. Furthermore, a new cascaded-structure classifier is also proposed, in which 11 types of hand gestures can be identified accurately using the newly defined features. Experimental results show that the success rate for the identification of the 11 gestures is significantly high

S-Convnet: A shallow convolutional neural network architecture for neuromuscular activity recognition using instantaneous high-density surface EMG images

The recent progress in recognizing low-resolution instantaneous high-density surface electromyography (HD-sEMG) images opens up new avenues for the development of more fluid and natural muscle-computer interfaces. However, the existing approaches employed a very large deep convolutional neural network (ConvNet) architecture and complex training schemes for HD-sEMG image recognition, which requires learning of ˃5.63 million(M) training parameters only during fine-tuning and pre-trained on a very large-scale labeled HD-sEMG training dataset, as a result, it makes high-end resource-bounded and computationally expensive. To overcome this problem, we propose S-ConvNet models, a simple yet efficient framework for learning instantaneous HD-sEMG images from scratch using random-initialization. Without using any pre-trained models, our proposed S-ConvNet demonstrate very competitive recognition accuracy to the more complex state of the art, while reducing learning parameters to only ≈ 2M and using ≈ 12 × smaller dataset. The experimental results proved that the proposed S-ConvNet is highly effective for learning discriminative features for instantaneous HD-sEMG image recognition, especially in the data and high-end resource-constrained scenarios

Sensor Developments for Electrophysiological Monitoring in Healthcare

Recent years have seen a renewal of interest in the development of sensor systems which can be used to monitor electrophysiological signals in a number of different settings. These include clinical, outside of the clinical setting with the subject ambulatory and going about their daily lives, and over long periods. The primary impetus for this is the challenge of providing healthcare for the ageing population based on home health monitoring, telehealth and telemedicine. Another stimulus is the demand for life sign monitoring of critical personnel such as fire fighters and military combatants. A related area of interest which, whilst not in the category of healthcare, utilises many of the same approaches, is that of sports physiology for both professional athletes and for recreation. Clinical diagnosis of conditions in, for example, cardiology and neurology remain based on conventional sensors, using established electrodes and well understood electrode placements. However, the demands of long term health monitoring, rehabilitation support and assistive technology for the disabled and elderly are leading research groups such as ours towards novel sensors, wearable and wireless enabled systems and flexible sensor arrays

Evaluation of surface EMG-based recognition algorithms for decoding hand movements

Myoelectric pattern recognition (MPR) to decode limb movements is an important advancement regarding the control of powered prostheses. However, this technology is not yet in wide clinical use. Improvements in MPR could potentially increase the functionality of powered prostheses. To this purpose, offline accuracy and processing time were measured over 44 features using six classifiers with the aim of determining new configurations of features and classifiers to improve the accuracy and response time of prosthetics control. An efficient feature set (FS: waveform length, correlation coefficient, Hjorth Parameters) was found to improve the motion recognition accuracy. Using the proposed FS significantly increased the performance of linear discriminant analysis, K-nearest neighbor, maximum likelihood estimation (MLE), and support vector machine by 5.5%, 5.7%, 6.3%, and 6.2%, respectively, when compared with the Hudgins\u27 set. Using the FS with MLE provided the largest improvement in offline accuracy over the Hudgins feature set, with minimal effect on the processing time. Among the 44 features tested, logarithmic root mean square and normalized logarithmic energy yielded the highest recognition rates (above 95%). We anticipate that this work will contribute to the development of more accurate surface EMG-based motor decoding systems for the control prosthetic hands