The Robotic-SixthFinger: An EMG Controlled Supernumerary Robotic Finger For Grasp Compensation In Chronic Stroke Patients

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Stroke is one of the leading causes of long term impairment. On average, every 40 seconds, someone in the United States suffers a stroke. Impairment of the hand grasping function is one of the common deficits after a stroke: approximately 60% of the stroke survivors suffer from some form of sensorimotor impairment associated with their hand. Recovering hand functions is of primary importance during the rehabilitation phase. However, only 5% to 20% of patients show a complete recovery of upper limb six months after the stroke. The latter phase of post-stroke rehabilitation is identified by the “compensation phase.” In this phase, functional recovery is based on the learning of newly acquired motor strategies to compensate the neurological deficit. These strategies may sometimes be neither ergonomic nor ecological, or may even increase pathological motor patterns, usually by worsening tonic flexion at the forearm of the paretic limb.

We expect to increase patients’ performances, with a focus on objects manipulation, thereby improving their independence in ADL, and simultaneously decreasing erroneous compensatory motor strategies for solving everyday tasks. The design of the Robotic-SixthFinger has been driven by robotic and rehabilitation teams, starting from patients requirements in improving upper limb functionality, when the motor deficit is unchangeable. This need is particularly felt by young and socially-active patients, for achieving better independence, and for continuing rehabilitation in a compensatory perspective.

We propose a novel solution to compensate hand grasping abilities of chronic stroke patients. The goal is to provide the patients with a wearable robotic extra-finger that can be used as grasp compensatory device for hemiparetic upper limbs to compensate for grasping in many Activities of Daily Living (ADL). The robotic device and the paretic limb act like the two parts of a gripper, cooperatively holding an object. The device is intrinsically-compliant, modular, underactuated and cable driven. It can be wrapped as bracelet to reduce the encumbrance when not being used. The motion of the robotic device can be controlled by using the eCap, an Electromyography (EMG) interface embedded in a cap. The user can control the device through contracting the frontalis muscle by moving his or her eyebrows upwards. The light weight and the complete wireless connection with the EMG interface guarantee a high portability and wearability. The performance characteristics of the device is measured through experimental set up and the shape adaptability was confirmed by grasping various objects with different shapes.

We tested the device through qualitative experiments based on ADL involving six chronic stroke patients. The prototype successfully enabled the patients to complete various bi-manual tasks. After the experiments, we asked the patients about their satisfaction and possible concern related to the proposed grasp compensatory robotic device. Results show that the proposed robotic device improves the autonomy of patients in ADL and allows them to complete tasks which were previously impossible to perform. Currently we are investigating the possibilities to introduce the device early in rehabilitation phase for the patients who are seeking improvements in their skills.

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  • ABOUT THE ENTRANT

  • Name:
    Irfan Hussain
  • Type of entry:
    team
    Team members:
    Irfan Hussain , Domenico Prattichizzo, Giovanni Spagnoletti and Gionata Salvietti
  • Profession:
    Engineer/Designer
  • Irfan is inspired by:
    Traditionally, wearable robotic structures have been mainly used in substitution of lost limbs (e.g., prosthetic limbs) or for human limb rehabilitation during six month after stroke (e.g., exoskeletons) . However, to the best of our knowledge, there is no robotic device for grasp compensation of chronic stroke patients. The design of our device is driven by the need to support the chronic stroke patients in activity of daily living (ADL).
  • Software used for this entry:
    COMSOL
  • Patent status:
    pending