Posts Tagged Closed-Loop

[Abstract] sEMG Bias-driven Functional Electrical Stimulation System for Upper Limb Stroke Rehabilitation


It is evident that the dominant therapy of functional electrical stimulation (FES) for stroke rehabilitation suffers from heavy dependency on therapists experience and lack of feedback from patients status, which decrease the patients’ voluntary participation, reducing the rehabilitation efficacy. This paper proposes a closed loop FES system using surface electromyography (sEMG) bias feedback from bilateral arms for enhancing upper-limb stroke rehabilitation. This wireless portable system consists of sEMG data acquisition and FES modules, the former is used to measure and analyze the subject’s bilateral arm motion intention and neuromuscular states in terms of their sEMG, the latter of multi-channel FES output is controlled via the sEMG bias of the bilateral arms. The system has been evaluated with experiments proving that the system can achieve 39.9 dB signal-to-noise ratio (SNR) in the lab environment, outperforming existing similar systems. The results also show that voluntary and active participation can be effectively employed to achieve different FES intensity for FES-assisted hand motions, demonstrating the potential for active stroke rehabilitation.
Published in: IEEE Sensors Journal ( Early Access ) Date of Publication: 18 June 2018

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[Abstract] Design and Test of a Closed-Loop FES System for Supporting Function of the Hemiparetic Hand Based on Automatic Detection Using the Microsoft Kinect Sensor

This paper describes the design of a FES system automatically controlled in a closed loop using a Microsoft Kinect sensor, for assisting both cylindrical grasping and hand opening. The feasibility of the system was evaluated in real-time in stroke patients with hand function deficits. A hand function exercise was designed in which the subjects performed an arm and hand exercise in sitting position. The subject had to grasp one of two differently sized cylindrical objects and move it forward or backwards in the sagittal plane. This exercise was performed with each cylinder with and without FES support. Results showed that the stroke patients were able to perform up to 29% more successful grasps when they were assisted by FES. Moreover, the hand grasp-and-hold and hold-and-release durations were shorter for the smaller of the two cylinders. FES was appropriately timed in more than 95% of all trials indicating successful closed loop FES control. Future studies should incorporate options for assisting forward reaching in order to target a larger group of stroke patients.

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[ARTICLE] An Exploratory Investigation on the Use of Closed-Loop Electrical Stimulation to Assist Individuals with Stroke to Perform Fine Movements with Their Hemiparetic Arm | Bionics and Biomimetics – Full Text HTML

Stroke is the leading cause of upper limb impairments resulting in disability. Modern rehabilitation includes training with robotic exoskeletons and functional electrical stimulation (FES). However, there is a gap in knowledge to define the detailed use of FES in stroke rehabilitation. In this paper, we explore applying closed-loop FES to the upper extremities of healthy volunteers and individuals with a hemiparetic arm resulting from stroke. We used a set of gyroscopes to monitor arm movements and used a non-linear controller, namely, the robust integral of the sign of the error (RISE), to assess the viability of controlling FES in closed loop. Further, we explored the application of closed-loop FES in improving functional tasks performed by individuals with stroke. Four healthy individuals of ages 27–32 years old and five individuals with stroke of ages 61–83 years old participated in this study. We used the Rehastim FES unit (Hasomed Ltd.) with real-time modulation of pulse width and amplitude. Both healthy and stroke individuals were tested in RISE-controlled single and multi-joint upper limb motions following first a sinusoidal trajectory. Individuals with stroke were also asked to perform the following functional tasks: picking up a basket, picking and placing an object on a table, cutting a pizza, pulling back a chair, eating with a spoon, as well as using a stapler and grasping a pen. Healthy individuals were instructed to keep their arm relaxed during the experiment. Most individuals with stroke were able to follow the sinusoid trajectories with their arm joints under the sole excitation of the closed-loop-controlled FES. One individual with stroke, who was unable to perform any of the functional tasks independently, succeeded in completing all the tasks when FES was used. Three other individuals with stroke, who were unable to complete a few tasks independently, completed some of them when FES was used. The remaining stroke participant was able to complete all tasks with and without FES. Our results suggest that individuals with a low Fugl–Meyer score or a higher level of disability may benefit the most with the use of closed-loop-controlled FES.


Stroke is the leading cause of upper limb disability and poor quality of life worldwide. Studies suggest that 3 months after stroke: 40% of stroke survivors suffer from significant upper extremity (UE), dysfunction of their affected arm, 40% have minor impairment, and only 20% retain full functionality (Buma et al., 2015). UE dysfunction includes motor deficits, functional deficits, and an inability to perform activities of daily living, thus increasing the burden of life (Feigin et al., 2008). Traditional rehabilitation techniques include high intensity-repetitive training, bilateral upper limb training, and constraint induced therapy to encourage neuroplasticity and early recovery (Intercollegiate Stroke Party, 2012). Functional electrical stimulation (FES) is a promising therapeutic treatment that complements the traditional therapy poststroke (Oujamaa et al., 2009). The most benefit seems to occur, however, when patients follow training schedules (Krakauer, 2006). Thus, to increase repetition and the efficacy of rehabilitation, the use of FES has been considered. FES allows the contraction of muscles independent of the central nervous system via electric current through surface or subcutaneous electrodes (Rushton, 1997).

Despite the beneficial results of FES, there is paucity of studies defining the detailed use of FES to achieve successful rehabilitation poststroke. Certainly, applying FES specifically to the limbs is challenging, because (1) commercially available stimulators employ an open-loop control, where the output movement is not fed back to the controller and (2) stimulator output has a pre-programed waveform of varying complexity, with no feedback or dynamic real-time alterations. On the other hand, graduated muscle contractions through closed-loop control may increase the precision, user safety, and robustness of FES because the output is modulated in real time according to a feedback loop (Zhang et al., 2013). A feedback loop could theoretically allow for finer control over the limb trajectory and thus ability to achieve complex maneuvers.

Even though there are numerous benefits, closed-loop FES systems are rarely available commercially, perhaps due to the challenge of implementing a control scheme that may be broadly applied to the non-linear and time varying behavior of muscles. Fatigue, spasticity, gravity, and training effects are other factors identified in distorting the controller performance (Ferrarin et al., 2001; Lynch and Popovic, 2012). In order to overcome these challenges, literature suggests different approaches. For example,Vette et al. (2007) and Sharma et al. (2012), suggested linear strategies with high gain feedback, but it cannot always guarantee system stability due to muscle non-linearity (Vette et al., 2007; Sharma et al., 2012). To compensate this flaw, machine learning (Vette et al., 2007) and iterated learning (Meadmore et al., 2014) algorithms have been applied. Other emerging promising non-linear strategies include sliding-mode control (Jezernik et al., 2004) and robust integral of the sign of the error (RISE) control (Sharma et al., 2009). Altogether, studies suggest that RISE control would be the preferred option of all due to the ease of implementation and stability. The RISE control guarantees stability under the assumption of a non-linear muscle model and appropriate controller gain constants (Sharma et al., 2009).

To the authors’ best knowledge, none of the studies have tested RISE methodology on hemiparetic arms in individuals with stroke. Hence, the first aim of this study was to assess the feasibility of applying closed-loop FES, utilizing the RISE controller algorithm during upper limb movements. We aimed to test it first in healthy individuals and next in individuals with stroke. We hypothesized that all participants (healthy and stroke affected) would be able to tolerate closed-loop FES. Tests with healthy subjects were first performed in order to verify that the tests could successfully be completed independently of the individual’s stroke condition. The healthy subjects verified the appropriateness of the procedure. These tests ensured that the potential inability of the individuals with stroke to complete tasks when assisted by FES was not inherently due to the adopted procedure.

Further, the second aim of this study was to assess the feasibility of using FES in individuals with stroke to augment their ability to perform functional tasks with their affected limb. Literature suggests that the majority of individuals with stroke develop abnormal flexion synergy in the UEs. It results in stereotyped, primitive mass movement pattern unsuitable to perform daily functional activities (Dipietro et al., 2007). For this study, stimulation of the affected side’s shoulder, elbow, and forearm muscles, namely, the anterior deltoid, infraspinatus, pectoralis major, biceps, triceps, and forearm extensor group, is hypothesized to aid participants to perform functional activities that are otherwise impossible because of this locked synergistic pattern poststroke.

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Figure 1. System block diagram for biceps muscle stimulation. The joint angle was calculated using two appropriately positioned strap-on gyroscopes, which communicated with the PC via a data acquisition device (DAQ) through USB. The PC communicated with the RehaStim device via a serial USB connection. Stimulation of other muscles had an analogous setup, but with alternate placement of the gyroscopes and electrodes.

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