A neural tracking and motor control approach to improve rehabilitation of upper limb movements

<p>Abstract</p> <p>Background</p> <p>Restoration of upper limb movements in subjects recovering from stroke is an essential keystone in rehabilitative practices. Rehabilitation of arm movements, in fact, is usually a far more difficult one as compared to that of lower extremities. For these reasons, researchers are developing new methods and technologies so that the rehabilitative process could be more accurate, rapid and easily accepted by the patient. This paper introduces the proof of concept for a new non-invasive FES-assisted rehabilitation system for the upper limb, called smartFES (sFES), where the electrical stimulation is controlled by a biologically inspired neural inverse dynamics model, fed by the kinematic information associated with the execution of a planar goal-oriented movement. More specifically, this work details two steps of the proposed system: an <it>ad hoc </it>markerless motion analysis algorithm for the estimation of kinematics, and a neural controller that drives a synthetic arm. The vision of the entire system is to acquire kinematics from the analysis of video sequences during planar arm movements and to use it together with a neural inverse dynamics model able to provide the patient with the electrical stimulation patterns needed to perform the movement with the assisted limb.</p> <p>Methods</p> <p>The markerless motion tracking system aims at localizing and monitoring the arm movement by tracking its silhouette. It uses a specifically designed motion estimation method, that we named Neural Snakes, which predicts the arm contour deformation as a first step for a silhouette extraction algorithm. The starting and ending points of the arm movement feed an Artificial Neural Controller, enclosing the muscular Hill's model, which solves the inverse dynamics to obtain the FES patterns needed to move a simulated arm from the starting point to the desired point. Both position error with respect to the requested arm trajectory and comparison between curvature factors have been calculated in order to determine the accuracy of the system.</p> <p>Results</p> <p>The proposed method has been tested on real data acquired during the execution of planar goal-oriented arm movements. Main results concern the capability of the system to accurately recreate the movement task by providing a synthetic arm model with the stimulation patterns estimated by the inverse dynamics model. In the simulation of movements with a length of ± 20 cm, the model has shown an unbiased angular error, and a mean (absolute) position error of about 1.5 cm, thus confirming the ability of the system to reliably drive the model to the desired targets. Moreover, the curvature factors of the factual human movements and of the reconstructed ones are similar, thus encouraging future developments of the system in terms of reproducibility of the desired movements.</p> <p>Conclusion</p> <p>A novel FES-assisted rehabilitation system for the upper limb is presented and two parts of it have been designed and tested. The system includes a markerless motion estimation algorithm, and a biologically inspired neural controller that drives a biomechanical arm model and provides the stimulation patterns that, in a future development, could be used to drive a smart Functional Electrical Stimulation system (sFES). The system is envisioned to help in the rehabilitation of post stroke hemiparetic patients, by assisting the movement of the paretic upper limb, once trained with a set of movements performed by the therapist or in virtual reality. Future work will include the application and testing of the stimulation patterns in real conditions.</p

Interaction between indoor occupational heat stress and environmental temperature elevations during heat waves

Occupational heat strain is a public health threat, and for outdoor industries there is a direct influence from elevated environmental temperatures during heat waves. However, the impact in indoor settings is more complex as industrial heat production and building architecture become factors of importance. Therefore, this study evaluated effects of heat waves on manufacturing productivity. Production halls in a manufacturing company were instrumented with 33 dataloggers to track air temperature and humidity. In addition, outdoor thermal conditions collected from a weather station next to the factory and daily productivity evaluated as overall equipment efficiency (OEE) were obtained, with interaction between productivity and thermal conditions analyzed before, during, and after four documented heat waves (average daily air temperature above 24°C on at least three consecutive days). Outdoor (before: 21.3° ± 4.6°C, during: 25.5° ± 4.3°C, and after: 19.8° ± 3.8°C) and indoor air temperatures (before: 30.4° ± 1.3°C, during: 32.8° ± 1.4°C, and after: 30.1° ± 1.4°C) were significantly elevated during the heat waves (p < 0.05). OEE was not different during the heat waves when compared with control, pre-heat-wave, and postheat- waveOEE. Reduced OEE was observed in 3-day periods following the second and fourth heat wave (p < 0.05). Indoor workers in settings with high industrial heat production are exposed to a significant thermal stress that may increase during heat waves, but the impact on productivity cannot be directly derived from outdoor factors. The significant decline in productivity immediately following two of the documented heat waves could relate to a cumulative effect of the thermal strain experienced during work combined with high heat stress in the recovery time between work shifts. © 2019 American Meteorological Society

Gait Recognition Based on EMG Information with Multiple Features

Part 10: Image UnderstandingInternational audienceIn order to evaluate the effects of time domain (TD) and frequency domain (FD) features as well as muscle number on gait classification recognition, eight channels of electromyography (EMG) signals were collected from four thigh and four lower leg muscles, and two TD features and two FD features were extracted in this study. The method of support vector machine (SVM) was presented to investigate the classification property. For the classification stability and accuracy, 3-fold cross validation was verified and selected to classify the lower limb gait. The results show that the FD features can obtain higher accuracy than TD features. In addition, accuracy of gait recognition increased with the augment of muscle number