Detecting Coronary Artery Disease Using Rest Seismocardiography and Gyrocardiography.

In this study, we present a non-invasive solution to identify patients with coronary artery disease (CAD) defined as ⩾50% stenosis in at least one coronary artery. The solution is based on the analysis of linear acceleration (seismocardiogram, SCG) and angular velocity (gyrocardiogram, GCG) of the heart recorded in the x, y, and z directional axes from an accelerometer/gyroscope sensor mounted on the sternum. The database was collected from 310 individuals through a multicenter study.

A Review of Force Myography Research and Development.

Information about limb movements can be used for monitoring physical activities or for human-machine-interface applications. In recent years, a technique called Force Myography (FMG) has gained ever-increasing traction among researchers to extract such information. FMG uses force sensors to register the variation of muscle stiffness patterns around a limb during different movements.

Effect of repetitive transcranial magnetic stimulation combined with robot-assisted training on wrist muscle activation post-stroke.

Objective: To compare the effects of active assisted wrist extension training, using a robotic exoskeleton (RW), with simultaneous 5 Hz (rTMS + RW) or Sham rTMS (Sham rTMS + RW) over the ipsilesional extensor carpi radialis motor cortical representation, on voluntary wrist muscle activation following stroke.

Methods: The two training conditions were completed at least one week apart in 13 participants >1-year post-stroke. Voluntary wrist extensor muscle activation (motor unit (MU) recruitment thresholds and firing rate modulation in a ramp-hold handgrip task), ipsilesional corticospinal excitability (motor evoked potential [MEP] amplitude) and transcallosal inhibition were measured Pre- and Post-training.

An Investigation on the Sampling Frequency of the Upper-Limb Force Myographic Signals.

Force myography (FMG) is an emerging method to register muscle activity of a limb using force sensors for human-machine interface and movement monitoring applications. Despite its newly gained popularity among researchers, many of its fundamental characteristics remain to be investigated. The aim of this study is to identify the minimum sampling frequency needed for recording upper-limb FMG signals without sacrificing signal integrity.

Does force myography recorded at the wrist correlate to resistance load levels during bicep curls?

Resistance strength training is a proven method to improve bone density and muscle strength. A solution capable of automatically detecting the resistance force level exerted by a user from a wrist-based device can offer great convenience to the trainee and hence facilitate a better training outcome. In this short communication, we present our investigation aimed at exploring if force myographic (FMG) signals recorded at the wrist can predict the relative resistance levels that are associated with different weights.

Counting Grasping Action Using Force Myography: An Exploratory Study With Healthy Individuals.

Background: Functional arm movements generally require grasping an object. The possibility of detecting and counting the action of grasping is believed to be of importance for individual with motor function deficits of the arm, as it could be an indication of the number of the functional arm movements performed by the individuals during rehabilitation. In this exploratory work, the feasibility of using armbands recording radial displacements of forearm muscles and tendons (ie, force myography, FMG) to estimate hand grasping with healthy individuals was investigated.

Exploration of Force Myography and surface Electromyography in hand gesture classification.

Whereas pressure sensors increasingly have received attention as a non-invasive interface for hand gesture recognition, their performance has not been comprehensively evaluated. This work examined the performance of hand gesture classification using Force Myography (FMG) and surface Electromyography (sEMG) technologies by performing 3 sets of 48 hand gestures using a prototyped FMG band and an array of commercial sEMG sensors worn both on the wrist and forearm simultaneously. The results show that the FMG band achieved classification accuracies as good as the high quality, commercially available, sEMG system on both wrist and forearm positions; specifically, by only using 8 Force Sensitive Resisters (FSRs), the FMG band achieved accuracies of 91.

A Single-Session Preliminary Evaluation of an Affordable BCI-Controlled Arm Exoskeleton and Motor-Proprioception Platform.

Traditional, hospital-based stroke rehabilitation can be labor-intensive and expensive. Furthermore, outcomes from rehabilitation are inconsistent across individuals and recovery is hard to predict. Given these uncertainties, numerous technological approaches have been tested in an effort to improve rehabilitation outcomes and reduce the cost of stroke rehabilitation.

Towards the development of a wearable feedback system for monitoring the activities of the upper-extremities.

Background: Body motion data registered by wearable sensors can provide objective feedback to patients on the effectiveness of the rehabilitation interventions they undergo. Such a feedback may motivate patients to keep increasing the amount of exercise they perform, thus facilitating their recovery during physical rehabilitation therapy. In this work, we propose a novel wearable and affordable system which can predict different postures of the upper-extremities by classifying force myographic (FMG) signals of the forearm in real-time.

A preliminary investigation assessing the viability of classifying hand postures in seniors.

Background: Fear of frailty is a main concern for seniors. Surface electromyography (sEMG) controlled assistive devices for the upper extremities could potentially be used to augment seniors' force while training their muscles and reduce their fear of frailty. In fact, these devices could both improve self confidence and facilitate independent leaving in domestic environments.

Surface EMG pattern recognition for real-time control of a wrist exoskeleton.

Background: Surface electromyography (sEMG) signals have been used in numerous studies for the classification of hand gestures and movements and successfully implemented in the position control of different prosthetic hands for amputees. sEMG could also potentially be used for controlling wearable devices which could assist persons with reduced muscle mass, such as those suffering from sarcopenia. While using sEMG for position control, estimation of the intended torque of the user could also provide sufficient information for an effective force control of the hand prosthesis or assistive device.