Within-socket Myoelectric Prediction of Continuous Ankle Kinematics for Control of a Powered Transtibial Prosthesis

Objective. Powered robotic prostheses create a need for natural-feeling user interfaces and robust control schemes. Here, we examined the ability of a nonlinear autoregressive model to continuously map the kinematics of a transtibial prosthesis and electromyographic (EMG) activity recorded within socket to the future estimates of the prosthetic ankle angle in three transtibial amputees. Approach. Model performance was examined across subjects during level treadmill ambulation as a function of the size of the EMG sampling window and the temporal \u27prediction\u27 interval between the EMG/kinematic input and the model\u27s estimate of future ankle angle to characterize the trade-off between model error, sampling window and prediction interval. Main results. Across subjects, deviations in the estimated ankle angle from the actual movement were robust to variations in the EMG sampling window and increased systematically with prediction interval. For prediction intervals up to 150 ms, the average error in the model estimate of ankle angle across the gait cycle was less than 6°. EMG contributions to the model prediction varied across subjects but were consistently localized to the transitions to/from single to double limb support and captured variations from the typical ankle kinematics during level walking. Significance. The use of an autoregressive modeling approach to continuously predict joint kinematics using natural residual muscle activity provides opportunities for direct (transparent) control of a prosthetic joint by the user. The model\u27s predictive capability could prove particularly useful for overcoming delays in signal processing and actuation of the prosthesis, providing a more biomimetic ankle response

Stand-alone wearable system for ubiquitous real-time monitoring of muscle activation potentials

Wearable technology is attracting most attention in healthcare for the acquisition of physiological signals. We propose a stand-alone wearable surface ElectroMyoGraphy (sEMG) system for monitoring the muscle activity in real time. With respect to other wearable sEMG devices, the proposed system includes circuits for detecting the muscle activation potentials and it embeds the complete real-time data processing, without using any external device. The system is optimized with respect to power consumption, with a measured battery life that allows for monitoring the activity during the day. Thanks to its compactness and energy autonomy, it can be used outdoor and it provides a pathway to valuable diagnostic data sets for patients during their own day-life. Our system has performances that are comparable to state-of-art wired equipment in the detection of muscle contractions with the advantage of being wearable, compact, and ubiquitous

A functional electrical stimulation system for human walking inspired by reflexive control principles

This study presents an innovative multichannel functional electrical stimulation gait-assist system which employs a well-established purely reflexive control algorithm, previously tested in a series of bipedal walking robots. In these robots, ground contact information was used to activate motors in the legs, generating a gait cycle similar to that of humans. Rather than developing a sophisticated closed-loop functional electrical stimulation control strategy for stepping, we have instead utilised our simple reflexive model where muscle activation is induced through transfer functions which translate sensory signals, predominantly ground contact information, into motor actions. The functionality of the functional electrical stimulation system was tested by analysis of the gait function of seven healthy volunteers during functional electrical stimulation–assisted treadmill walking compared to unassisted walking. The results demonstrated that the system was successful in synchronising muscle activation throughout the gait cycle and was able to promote functional hip and ankle movements. Overall, the study demonstrates the potential of human-inspired robotic systems in the design of assistive devices for bipedal walking

Heterogeneous data fusion for brain psychology applications

This thesis aims to apply Empirical Mode Decomposition (EMD), Multiscale Entropy (MSE), and collaborative adaptive filters for the monitoring of different brain consciousness states. Both block based and online approaches are investigated, and a possible extension to the monitoring and identification of Electromyograph (EMG) states is provided. Firstly, EMD is employed as a multiscale time-frequency data driven tool to decompose a signal into a number of band-limited oscillatory components; its data driven nature makes EMD an ideal candidate for the analysis of nonlinear and non-stationary data. This methodology is further extended to process multichannel real world data, by making use of recent theoretical advances in complex and multivariate EMD. It is shown that this can be used to robustly measure higher order features in multichannel recordings to robustly indicate ‘QBD’. In the next stage, analysis is performed in an information theory setting on multiple scales in time, using MSE. This enables an insight into the complexity of real world recordings. The results of the MSE analysis and the corresponding statistical analysis show a clear difference in MSE between the patients in different brain consciousness states. Finally, an online method for the assessment of the underlying signal nature is studied. This method is based on a collaborative adaptive filtering approach, and is shown to be able to approximately quantify the degree of signal nonlinearity, sparsity, and non-circularity relative to the constituent subfilters. To further illustrate the usefulness of the proposed data driven multiscale signal processing methodology, the final case study considers a human-robot interface based on a multichannel EMG analysis. A preliminary analysis shows that the same methodology as that applied to the analysis of brain cognitive states gives robust and accurate results. The analysis, simulations, and the scope of applications presented suggest great potential of the proposed multiscale data processing framework for feature extraction in multichannel data analysis. Directions for future work include further development of real-time feature map approaches and their use across brain-computer and brain-machine interface applications

CES-513 Stages for Developing Control Systems using EMG and EEG Signals: A survey

Bio-signals such as EMG (Electromyography), EEG (Electroencephalography), EOG (Electrooculogram), ECG (Electrocardiogram) have been deployed recently to develop control systems for improving the quality of life of disabled and elderly people. This technical report aims to review the current deployment of these state of the art control systems and explain some challenge issues. In particular, the stages for developing EMG and EEG based control systems are categorized, namely data acquisition, data segmentation, feature extraction, classification, and controller. Some related Bio-control applications are outlined. Finally a brief conclusion is summarized.

Advanced Signal Processing in Wearable Sensors for Health Monitoring

Smart, wearables devices on a miniature scale are becoming increasingly widely available, typically in the form of smart watches and other connected devices. Consequently, devices to assist in measurements such as electroencephalography (EEG), electrocardiogram (ECG), electromyography (EMG), blood pressure (BP), photoplethysmography (PPG), heart rhythm, respiration rate, apnoea, and motion detection are becoming more available, and play a significant role in healthcare monitoring. The industry is placing great emphasis on making these devices and technologies available on smart devices such as phones and watches. Such measurements are clinically and scientifically useful for real-time monitoring, long-term care, and diagnosis and therapeutic techniques. However, a pertaining issue is that recorded data are usually noisy, contain many artefacts, and are affected by external factors such as movements and physical conditions. In order to obtain accurate and meaningful indicators, the signal has to be processed and conditioned such that the measurements are accurate and free from noise and disturbances. In this context, many researchers have utilized recent technological advances in wearable sensors and signal processing to develop smart and accurate wearable devices for clinical applications. The processing and analysis of physiological signals is a key issue for these smart wearable devices. Consequently, ongoing work in this field of study includes research on filtration, quality checking, signal transformation and decomposition, feature extraction and, most recently, machine learning-based methods

A survey on bio-signal analysis for human-robot interaction

The use of bio-signals analysis in human-robot interaction is rapidly increasing. There is an urgent demand for it in various applications, including health care, rehabilitation, research, technology, and manufacturing. Despite several state-of-the-art bio-signals analyses in human-robot interaction (HRI) research, it is unclear which one is the best. In this paper, the following topics will be discussed: robotic systems should be given priority in the rehabilitation and aid of amputees and disabled people; second, domains of feature extraction approaches now in use, which are divided into three main sections (time, frequency, and time-frequency). The various domains will be discussed, then a discussion of each domain's benefits and drawbacks, and finally, a recommendation for a new strategy for robotic systems