174 research outputs found
Muscle Connectivity Analysis for Hand Gesture Recognition via sEMG
Physiological measurement like surface electromyography (sEMG) allows a deeper insight on interactions among subsystems during the human motion coordination. In this paper, we aim to investigate such interactions via functional muscle networks during hand movements, especially when different hand gestures are performed. It is achieved by considering muscle connectivities using Granger Prediction of paired sEMG signals, which were recorded from extrinsic muscles of the upper limb, while participants were sitting upright and performing hand gestures. It is found that by using muscle connectivities obtained by applying the method of Granger Prediction as features, although individual differences exist among subjects, significant connections between pairs of muscles were observed through permutation tests at a group level. Graph theory based on the overall statistical result was used to visualise functional networks by considering all the significant connections which were not bidirectional. We found two distinct networks can be used to represent the differences between two hand gestures. Such insight of functional networks can be a potential candidate to interpret the relationships between muscle pairs, which is helpful for decoding hand gestures
A Wearable Ultra-Low-Power sEMG-Triggered Ultrasound System for Long-Term Muscle Activity Monitoring
Surface electromyography (sEMG) is a well-established approach to monitor
muscular activity on wearable and resource-constrained devices. However, when
measuring deeper muscles, its low signal-to-noise ratio (SNR), high signal
attenuation, and crosstalk degrade sensing performance. Ultrasound (US)
complements sEMG effectively with its higher SNR at high penetration depths. In
fact, combining US and sEMG improves the accuracy of muscle dynamic assessment,
compared to using only one modality. However, the power envelope of US hardware
is considerably higher than that of sEMG, thus inflating energy consumption and
reducing the battery life. This work proposes a wearable solution that
integrates both modalities and utilizes an EMG-driven wake-up approach to
achieve ultra-low power consumption as needed for wearable long-term
monitoring. We integrate two wearable state-of-the-art (SoA) US and ExG
biosignal acquisition devices to acquire time-synchronized measurements of the
short head of the biceps. To minimize power consumption, the US probe is kept
in a sleep state when there is no muscle activity. sEMG data are processed on
the probe (filtering, envelope extraction and thresholding) to identify muscle
activity and generate a trigger to wake-up the US counterpart. The US
acquisition starts before muscle fascicles displacement thanks to a triggering
time faster than the electromechanical delay (30-100 ms) between the
neuromuscular junction stimulation and the muscle contraction. Assuming a
muscle contraction of 200 ms at a contraction rate of 1 Hz, the proposed
approach enables more than 59% energy saving (with a full-system average power
consumption of 12.2 mW) as compared to operating both sEMG and US continuously.Comment: 4 pages, 5 figures, 1 table, 2023 IEEE International Ultrasonics
Symposiu
Transradial Amputee Gesture Classification Using an Optimal Number of sEMG Sensors: An Approach Using ICA Clustering
© 2001-2011 IEEE. Surface electromyography (sEMG)-based pattern recognition studies have been widely used to improve the classification accuracy of upper limb gestures. Information extracted from multiple sensors of the sEMG recording sites can be used as inputs to control powered upper limb prostheses. However, usage of multiple EMG sensors on the prosthetic hand is not practical and makes it difficult for amputees due to electrode shift/movement, and often amputees feel discomfort in wearing sEMG sensor array. Instead, using fewer numbers of sensors would greatly improve the controllability of prosthetic devices and it would add dexterity and flexibility in their operation. In this paper, we propose a novel myoelectric control technique for identification of various gestures using the minimum number of sensors based on independent component analysis (ICA) and Icasso clustering. The proposed method is a model-based approach where a combination of source separation and Icasso clustering was utilized to improve the classification performance of independent finger movements for transradial amputee subjects. Two sEMG sensor combinations were investigated based on the muscle morphology and Icasso clustering and compared to Sequential Forward Selection (SFS) and greedy search algorithm. The performance of the proposed method has been validated with five transradial amputees, which reports a higher classification accuracy (> 95%). The outcome of this study encourages possible extension of the proposed approach to real time prosthetic applications
Low-cost wearable multichannel surface EMG acquisition for prosthetic hand control
Prosthetic hand control based on the acquisition
and processing of surface electromyography signals (sEMG) is a
well-established method that makes use of the electric potentials
evoked by the physiological contraction processes of one or more
muscles. Furthermore intelligent mobile medical devices are on
the brink of introducing safe and highly sophisticated systems to
help a broad patient community to regain a considerable amount
of life quality. The major challenges which are inherent in such
integrated system’s design are mainly to be found in obtaining a
compact system with a long mobile autonomy, capable of
delivering the required signal requirements for EMG based
prosthetic control with up to 32 simultaneous acquisition
channels and – with an eye on a possible future exploitation as a
medical device – a proper perspective on a low priced system.
Therefore, according to these requirements we present a wireless,
mobile platform for acquisition and communication of sEMG
signals embedded into a complete mobile control system
structure. This environment further includes a portable device
such as a laptop providing the necessary computational power
for the control and a commercially available robotic handprosthesis.
Means of communication among those devices are
based on the Bluetooth standard. We show, that the developed
low cost mobile device can be used for proper prosthesis control
and that the device can rely on a continuous operation for the
usual daily life usage of a patient
Surface EMG-Based Inter-Session/Inter-Subject Gesture Recognition by Leveraging Lightweight All-ConvNet and Transfer Learning
Gesture recognition using low-resolution instantaneous HD-sEMG images opens
up new avenues for the development of more fluid and natural muscle-computer
interfaces. However, the data variability between inter-session and
inter-subject scenarios presents a great challenge. The existing approaches
employed very large and complex deep ConvNet or 2SRNN-based domain adaptation
methods to approximate the distribution shift caused by these inter-session and
inter-subject data variability. Hence, these methods also require learning over
millions of training parameters and a large pre-trained and target domain
dataset in both the pre-training and adaptation stages. As a result, it makes
high-end resource-bounded and computationally very expensive for deployment in
real-time applications. To overcome this problem, we propose a lightweight
All-ConvNet+TL model that leverages lightweight All-ConvNet and transfer
learning (TL) for the enhancement of inter-session and inter-subject gesture
recognition performance. The All-ConvNet+TL model consists solely of
convolutional layers, a simple yet efficient framework for learning invariant
and discriminative representations to address the distribution shifts caused by
inter-session and inter-subject data variability. Experiments on four datasets
demonstrate that our proposed methods outperform the most complex existing
approaches by a large margin and achieve state-of-the-art results on
inter-session and inter-subject scenarios and perform on par or competitively
on intra-session gesture recognition. These performance gaps increase even more
when a tiny amount (e.g., a single trial) of data is available on the target
domain for adaptation. These outstanding experimental results provide evidence
that the current state-of-the-art models may be overparameterized for
sEMG-based inter-session and inter-subject gesture recognition tasks
Hand Gestures Recognition for Human-Machine Interfaces: A Low-Power Bio-Inspired Armband
Hand gesture recognition has recently increased its popularity as Human-Machine Interface (HMI) in the biomedical field. Indeed, it can be performed involving many different non-invasive techniques, e.g., surface ElectroMyoGraphy (sEMG) or PhotoPlethysmoGraphy (PPG). In the last few years, the interest demonstrated by both academia and industry brought to a continuous spawning of commercial and custom wearable devices, which tried to address different challenges in many application fields, from tele-rehabilitation to sign language recognition. In this work, we propose a novel 7-channel sEMG armband, which can be employed as HMI for both serious gaming control and rehabilitation support. In particular, we designed the prototype focusing on the capability of our device to compute the Average Threshold Crossing (ATC) parameter, which is evaluated by counting how many times the sEMG signal crosses a threshold during a fixed time duration (i.e., 130 ms), directly on the wearable device. Exploiting the event-driven characteristic of the ATC, our armband is able to accomplish the on-board prediction of common hand gestures requiring less power w.r.t. state of the art devices. At the end of an acquisition campaign that involved the participation of 26 people, we obtained an average classifier accuracy of 91.9% when aiming to recognize in real time 8 active hand gestures plus the idle state. Furthermore, with 2.92mA of current absorption during active functioning and 1.34mA prediction latency, this prototype confirmed our expectations and can be an appealing solution for long-term (up to 60 h) medical and consumer applications
A virtual reality input device for sports-related rehabilitation
Abstract. This work entails the hardware design, manufacturing and implementation of a VR controller device tailored for people with specific sports-related injuries. The target case of this thesis is the tennis elbow injury, where the designed controller helps them interface easily to the VR environment that is designed for their therapy.
The sensors used are carefully selected in order to adequately capture the therapy exercise movements related to this kind of injury. For example, the use FSRs (Force Sensitive Resistors) that are put on the surface of a test object helps to detect a grasp during the exercise.
The hardware design and manufacturing was done for a VR controller device that would give the desired performance, using Arduino IDE for its software development. In addition to this, the design of the VR environment allowed for an immersive VR experience for the rehabilitation.
An experiment was carried out with eight participants, where they were asked to perform two exercises that involve grasping the test object. A series of questions were asked to them as part of the experimental evaluation. The results showed positive indications about the participants’ experience
Optimization of Forcemyography Sensor Placement for Arm Movement Recognition
How to design an optimal wearable device for human movement recognition is
vital to reliable and accurate human-machine collaboration. Previous works
mainly fabricate wearable devices heuristically. Instead, this paper raises an
academic question: can we design an optimization algorithm to optimize the
fabrication of wearable devices such as figuring out the best sensor
arrangement automatically? Specifically, this work focuses on optimizing the
placement of Forcemyography (FMG) sensors for FMG armbands in the application
of arm movement recognition. Firstly, based on graph theory, the armband is
modeled considering sensors' signals and connectivity. Then, a Graph-based
Armband Modeling Network (GAM-Net) is introduced for arm movement recognition.
Afterward, the sensor placement optimization for FMG armbands is formulated and
an optimization algorithm with greedy local search is proposed. To study the
effectiveness of our optimization algorithm, a dataset for mechanical
maintenance tasks using FMG armbands with 16 sensors is collected. Our
experiments show that using only 4 sensors optimized with our algorithm can
help maintain a comparable recognition accuracy to using all sensors. Finally,
the optimized sensor placement result is verified from a physiological view.
This work would like to shed light on the automatic fabrication of wearable
devices considering downstream tasks, such as human biological signal
collection and movement recognition. Our code and dataset are available at
https://github.com/JerryX1110/IROS22-FMG-Sensor-OptimizationComment: 6 pages, 10 figures, Accepted by IROS22 (The 2022 IEEE/RSJ
International Conference on Intelligent Robots and Systems (IROS
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