Intimate interfaces in action: assessing the usability and subtlety of emg-based motionless gestures

Mobile communication devices, such as mobile phones and networked personal digital assistants (PDAs), allow users to be constantly connected and communicate anywhere and at any time, often resulting in personal and private communication taking place in public spaces. This private -- public contrast can be problematic. As a remedy, we promote intimate interfaces: interfaces that allow subtle and minimal mobile interaction, without disruption of the surrounding environment. In particular, motionless gestures sensed through the electromyographic (EMG) signal have been proposed as a solution to allow subtle input in a mobile context. In this paper we present an expansion of the work on EMG-based motionless gestures including (1) a novel study of their usability in a mobile context for controlling a realistic, multimodal interface and (2) a formal assessment of how noticeable they are to informed observers. Experimental results confirm that subtle gestures can be profitably used within a multimodal interface and that it is difficult for observers to guess when someone is performing a gesture, confirming the hypothesis of subtlety

Data Quality and Reliability Assessment of Wearable EMG and IMU Sensor for Construction Activity Recognition

The workforce shortage is one of the significant problems in the construction industry. To overcome the challenges due to workforce shortage, various researchers have proposed wearable sensor-based systems in the area of construction safety and health. Although sensors provide rich and detailed information, not all sensors can be used for construction applications. This study evaluates the data quality and reliability of forearm electromyography (EMG) and inertial measurement unit (IMU) of armband sensors for construction activity classification. To achieve the proposed objective, the forearm EMG and IMU data collected from eight participants while performing construction activities such as screwing, wrenching, lifting, and carrying on two different days were used to analyze the data quality and reliability for activity recognition through seven different experiments. The results of these experiments show that the armband sensor data quality is comparable to the conventional EMG and IMU sensors with excellent relative and absolute reliability between trials for all the five activities. The activity classification results were highly reliable, with minimal change in classification accuracies for both the days. Moreover, the results conclude that the combined EMG and IMU models classify activities with higher accuracies compared to individual sensor models

Kvantitativna analiza pokreta u rehabilitaciji neuroloških poremećaja korišćenjem vizuelnih i nosivih senzora.

Neuroloska oboljenja, kao sto su Parkinsonova bolest i slog, dovode do ozbiljnih motornih poremecaja, smanjuju kvalitet zivota pacijenata i mogu da uzrokuju smrt. Rana dijagnoza i adekvatno lecenje su krucijalni faktori za drzanje bolesti pod kontrolom, kako bi se omogucio normalan svakodnevni zivot pacijenata. Lecenje neurolo skih bolesti obicno ukljucuje rehabilitacionu terapiju i terapiju lekovima, koje se prilagodavaju u skladu sa stanjem pacijenta tokom vremena. Tradicionalne tehnike evaluacije u dijagnozi i monitoringu neuroloskih bolesti oslanjaju se na klinicke evaluacione alate, tacnije specijalno dizajnirane klinicke testove i skale. Medutim, iako su korisne i najcesce koriscene, klinicke skale su sklone subjektivnim ocenama i nepreciznoj interpretaciji performanse pacijenta...Neurological disorders, such as Parkinson's disease (PD) and stroke, lead to serious motor disabilities, decrease the patients' quality of life and can cause the mortality. Early diagnosis and adequate disease treatment are thus crucial factors towards keeping the disease under control in order to enable the normal every-day life of patients. The treatment of neurological disorders usually includes the rehabilitation therapy and drug treatment, that are adapted based on the evaluation of the patient state over time. Conventional evaluation techniques for diagnosis and monitoring in neurological disorders rely on the clinical assessment tools i.e. specially designed clinical tests and scales. However, although benecial and commonly used, those scales are descriptive (qualitative), primarily intended to be carried out by a trained neurologist, and are prone to subjective rating and imprecise interpretation of patient's performance..

The "Federica" hand: a simple, very efficient prothesis

Hand prostheses partially restore hand appearance and functionalities. Not everyone can afford expensive prostheses and many low-cost prostheses have been proposed. In particular, 3D printers have provided great opportunities by simplifying the manufacturing process and reducing costs. Generally, active prostheses use multiple motors for fingers movement and are controlled by electromyographic (EMG) signals. The "Federica" hand is a single motor prosthesis, equipped with an adaptive grasp and controlled by a force-myographic signal. The "Federica" hand is 3D printed and has an anthropomorphic morphology with five fingers, each consisting of three phalanges. The movement generated by a single servomotor is transmitted to the fingers by inextensible tendons that form a closed chain; practically, no springs are used for passive hand opening. A differential mechanical system simultaneously distributes the motor force in predefined portions on each finger, regardless of their actual positions. Proportional control of hand closure is achieved by measuring the contraction of residual limb muscles by means of a force sensor, replacing the EMG. The electrical current of the servomotor is monitored to provide the user with a sensory feedback of the grip force, through a small vibration motor. A simple Arduino board was adopted as processing unit. The differential mechanism guarantees an efficient transfer of mechanical energy from the motor to the fingers and a secure grasp of any object, regardless of its shape and deformability. The force sensor, being extremely thin, can be easily embedded into the prosthesis socket and positioned on both muscles and tendons; it offers some advantages over the EMG as it does not require any electrical contact or signal processing to extract information about the muscle contraction intensity. The grip speed is high enough to allow the user to grab objects on the fly: from the muscle trigger until to the complete hand closure, "Federica" takes about half a second. The cost of the device is about 100 US$. Preliminary tests carried out on a patient with transcarpal amputation, showed high performances in controlling the prosthesis, after a very rapid training session. The "Federica" hand turned out to be a lightweight, low-cost and extremely efficient prosthesis. The project is intended to be open-source: all the information needed to produce the prosthesis (e.g. CAD files, circuit schematics, software) can be downloaded from a public repository. Thus, allowing everyone to use the "Federica" hand and customize or improve it

iMOVE: Development of a hybrid control interface based on sEMG and movement signals for an assistive robotic manipulator

For many people with upper limb disabilities, simple activities of daily living such as drinking, opening a door, or pushing an elevator button require the assistance of a caregiver; which reduces the independence of the individual. Assistive robotic systems controlled via human-robot interface could enable these people to perform this kind of tasks autonomously again and thereby increase their independence and quality of life. Moreover, this interface could encourage rehabilitation of motor functions because the individual would require to perform its remaining body movements and muscle activity to provide control signals. This project aims at developing a novel hybrid control interface that combines remaining movements and muscle activity of the upper body to control position and impedance of a robotic manipulator. This thesis presents a Cartesian position control system for KINOVA Gen3 robotic arm, which performs a proportional-derivative control low based to the Jacobian transpose method, that does not require inverse kinematics. A second control is proposed to change the robot’s rigidity in real-time based on measurements of muscle activity (sEMG). This control allows the user to modulate the robot’s impedance while performing a task. Moreover, it presents a body-machine interface that maps the motions of the upper body (head and shoulders) to the space of robot control signals. Its uses the principal component analysis algorithm for dimensionality reduction. The results demonstrate that combining the three methods presented above, the user can control robot positions with head and shoulders movements, while also adapting the robot’s impedance depending on its muscle activation. In the future work the performance of this system is going to be tested in patients with severe movement impairments

Systematic review of textile-based electrodes for long-term and continuous surface electromyography recording

This systematic review concerns the use of smart textiles enabled applications based on myoelectric activity. Electromyography (EMG) is the technique for recording and evaluating electric signals related to muscle activity (myoelectric). EMG is a well-established technique that provides a wealth of information for clinical diagnosis, monitoring, and treatment. Introducing sensor systems that allow for ubiquitous monitoring of health conditions using textile integrated solutions not only opens possibilities for ambulatory, long-term, and continuous health monitoring outside the hospital, but also for autonomous self-administration. Textile-based electrodes have demonstrated potential as a fully operational alternative to \u27standard\u27 Ag/AgCl electrodes for recording surface electromyography (sEMG) signals. As a substitute for Ag/AgCl electrodes fastened to the skin by taping or pre-gluing adhesive, textile-based electrodes have the advantages of being soft, flexible, and air permeable; thus, they have advantages in medicine and health monitoring, especially when self-administration, real-time, and long-term monitoring is required. Such advances have been achieved through various smart textile techniques; for instance, adding functions in textiles, including fibers, yarns, and fabrics, and various methods for incorporating functionality into textiles, such as knitting, weaving, embroidery, and coating. In this work, we reviewed articles from a textile perspective to provide an overview of sEMG applications enabled by smart textile strategies. The overview is based on a literature evaluation of 41 articles published in both peer-reviewed journals and conference proceedings focusing on electrode materials, fabrication methods, construction, and sEMG applications. We introduce four textile integration levels to further describe the various textile electrode sEMG applications reported in the reviewed literature. We conclude with suggestions for future work along with recommendations for the reporting of essential benchmarking information in current and future textile electrode applications

Novel Muscle Monitoring by Radiomyography(RMG) and Application to Hand Gesture Recognition

Conventional electromyography (EMG) measures the continuous neural activity during muscle contraction, but lacks explicit quantification of the actual contraction. Mechanomyography (MMG) and accelerometers only measure body surface motion, while ultrasound, CT-scan and MRI are restricted to in-clinic snapshots. Here we propose a novel radiomyography (RMG) for continuous muscle actuation sensing that can be wearable and touchless, capturing both superficial and deep muscle groups. We verified RMG experimentally by a forearm wearable sensor for detailed hand gesture recognition. We first converted the radio sensing outputs to the time-frequency spectrogram, and then employed the vision transformer (ViT) deep learning network as the classification model, which can recognize 23 gestures with an average accuracy up to 99% on 8 subjects. By transfer learning, high adaptivity to user difference and sensor variation were achieved at an average accuracy up to 97%. We further demonstrated RMG to monitor eye and leg muscles and achieved high accuracy for eye movement and body postures tracking. RMG can be used with synchronous EMG to derive stimulation-actuation waveforms for many future applications in kinesiology, physiotherapy, rehabilitation, and human-machine interface

Spectral Collaborative Representation based Classification for hand gestures recognition on electromyography signals

AbstractThe classification of the bio-signal has been used for various purposes in the literature as they are versatile in diagnosis of anomalies, improvement of overall health and sport performance and creating intuitive human computer interfaces. However, automatic identification of the signal patterns on a streaming real-time signal requires a series of complex procedures. A plethora of heuristic methods, such as neural networks and fuzzy systems, have been proposed as a solution. These methods stipulate certain conditions, such as preconditioning the signals, manual feature selection and large number of training samples.In this study, we introduce a novel variant and application of the Collaborative Representation based Classification (CRC) in spectral domain for recognition of hand gestures using raw surface electromyography (EMG) signals. The CRC based methods do not require large number of training samples for an efficient pattern classification. Additionally, we present a training procedure in which a high end subspace clustering method is employed for clustering the representative samples into their corresponding class labels. Thereby, the need for feature extraction and spotting patterns manually on the training samples is obviated.We presented the intuitive use of spectral features via circulant matrices. The proposed Spectral Collaborative Representation based Classification (SCRC) is able to recognize gestures with higher levels of accuracy for a fairly rich gesture set compared to the available methods. The worst recognition result which is the best in the literature is obtained as 97.3% among the four sets of the experiments for each hand gestures. The recognition results are reported with a substantial number of experiments and labeling computation