A Comparison of Day-Long Recording Stability and Muscle Force Prediction between BSN-Based Mechanomyography and Electromyography

Gavriel, Constantinos; Faisal, A. Aldo

doi:10.1109/bsn.2014.23

Cited by 16 publications

(8 citation statements)

References 30 publications

(29 reference statements)

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“…We evaluated our wink-based system against two other approaches: Myo armband EMG gesture triggers and voice based command which are alternative modalities to control an assistive glove. However we note that the suitability for EMG in day-long use has skin-contact based limitations [27], as well as electrical noise and humidity of the environment.…”

Section: Discussionmentioning

confidence: 95%

“Wink to grasp” — comparing eye, voice & EMG gesture control of grasp with soft-robotic gloves

Noronha

Dziemian

Zito

et al. 2017

2017 International Conference on Rehabilitation Robotics (ICORR)

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The ability of robotic rehabilitation devices to support paralysed end-users is ultimately limited by the degree to which human-machine-interaction is designed to be effective and efficient in translating user intention into robotic action. Specifically, we evaluate the novel possibility of binocular eye-tracking technology to detect voluntary winks from involuntary blink commands, to establish winks as a novel low-latency control signal to trigger robotic action. By wearing binocular eye-tracking glasses we enable users to directly observe their environment or the actuator and trigger movement actions, without having to interact with a visual display unit or user interface. We compare our novel approach to two conventional approaches for controlling robotic devices based on electromyo-graphy (EMG) and speech-based human-computer interaction technology. We present an integrated software framework based on ROS that allows transparent integration of these multiple modalities with a robotic system. We use a soft-robotic SEM glove (Bioservo Technologies AB, Sweden) to evaluate how the 3 modalities support the performance and subjective experience of the end-user when movement assisted. All 3 modalities are evaluated in streaming, closed-loop control operation for grasping physical objects. We find that wink control shows the lowest error rate mean with lowest standard deviation of (0.23 ± 0.07, mean ± SEM) followed by speech control (0.35 ± 0. 13) and EMG gesture control (using the Myo armband by Thalamic Labs), with the highest mean and standard deviation (0.46 ± 0.16). We conclude that with our novel own developed eye-tracking based approach to control assistive technologies is a well suited alternative to conventional approaches, especially when combined with 3D eye-tracking based robotic end-point control.

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Section: Discussionmentioning

confidence: 95%

“Wink to grasp” — comparing eye, voice & EMG gesture control of grasp with soft-robotic gloves

Noronha

Dziemian

Zito

et al. 2017

2017 International Conference on Rehabilitation Robotics (ICORR)

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“…The addition of temporal structure has a direct application to prosthetic control: We previously showed that learning the likelihood of future movement sequences in natural behaviour allows to better decode movement intention by combining the estimate from the measurements on the body (e.g. EMG or MMG [19], [20]) with the known temporal structure of the behaviour [21], [22].…”

Section: Discussionmentioning

confidence: 99%

Towards sparse coding of natural movements for neuroprosthetics and brain-machine interfaces

Thomik¹,

Fenske

Faisal

2015

2015 7th International IEEE/EMBS Conference on Neural Engineering (NER)

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Abstract-The correlation structure of natural hand & finger movements suggests that their motion is controlled in a lower-dimensional space than would be possible given their mechanical nature. Yet, it is unclear whether this low dimensional embedding is relevant to how the brain represents motor actions and how we can decode it for Brain-Machine Interface applications. We collected large data set of natural hand movement kinematics and analysed it using a novel sparse coding and dictionary learning approach -Sparse Movement Decomposition (SMD), which captures the embedding of the data in terms of spatial and temporal structure. We show that our sparse codes over natural movement statistics give a more parsimonious representation than the simple correlation structure. This suggest that, like V1 neuron receptive fields can be predicted from sparse code over natural image statistics, motor control may be encoded in such a manner. We further show how our sparse coding can help understand the temporal structure of behaviour, and thus our technique may be used for behavioural fingerprinting in diagnostics and for more naturalistic neuroprosthetic control.

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“…approach to neurotechnology [33]- [36], where we rely on smart software and understanding of the underlying neurological challenge, instead on investing in expensive high-fidelity hardware. Beyond heart diagnostics related applications of this technology, our approach could be also integrated in our ETHO1 wireless kinematic body sensor network [37]- [39] using the accelerometer sensor of a single node when strapped on the chest of a subject. This will enable us to pursue large scale field studies with patient and control groups at an affordable price while recording a wider range of human behaviour modalities (heart activity, motor movement kinematics and neurological signals) in unconstrained, daily-life environments.…”

Section: Discussionmentioning

confidence: 99%

Smartphone as an ultra-low cost medical tricorder for real-time cardiological measurements via ballistocardiography

Gavriel

Parker

Faisal

2015

2015 IEEE 12th International Conference on Wearable and Implantable Body Sensor Networks (BSN)

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In this preliminary study, we investigate the potential use of smartphones as portable heart-monitoring devices that can capture and analyse heart activity in real time. We have developed a smartphone application called "Medical Tricorder" that can exploit smartphone's inertial sensors and when placed on a subject's chest, it can efficiently capture the motion patterns caused by the mechanical activity of the heart. Using the measured ballistocardiograph signal (BCG), the application can efficiently extract the heart rate in real time while matching the performance of clinical-grade electrocardiographs (ECG). Although the BCG signal can provide much richer information regarding the mechanical aspects of the human heart, we have developed a method of mapping the chest BCG signal into an ECG signal, which can be made directly available to clinicians for diagnostics. Comparing the estimated ECG signal to empirical data from cardiovascular diseases, may allow detection of heart abnormalities at a very early stage without any medical staff involvement. Our method opens up the potential of turning smartphones into portable healthcare systems which can provide patients and general public an easy access to continuous healthcare monitoring. Additionally, given that our solution is mainly software based, it can be deployed on smartphones around the world with minimal costs.

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A Comparison of Day-Long Recording Stability and Muscle Force Prediction between BSN-Based Mechanomyography and Electromyography

Cited by 16 publications

References 30 publications

“Wink to grasp” — comparing eye, voice & EMG gesture control of grasp with soft-robotic gloves

“Wink to grasp” — comparing eye, voice & EMG gesture control of grasp with soft-robotic gloves

Towards sparse coding of natural movements for neuroprosthetics and brain-machine interfaces

Smartphone as an ultra-low cost medical tricorder for real-time cardiological measurements via ballistocardiography

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