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1. Design of Bio-Exoskeleton for Elbow Rehabilitation

   https://doi.org/10.1115/DMD2021-1035  

   Delgado, Pablo; Arachchige Don, Thisath Attampola; Gomez, Jesus; Miranda, Virgil; Yihun, Yimesker (May 2020, 2021 Design of Medical Devices Conference)

   null (Ed.)

   In this study, a methodology for designing a task-based exoskeleton which can recreate the end-effector trajectory of a given limb during a rehabilitation task/movement is presented. The exoskeleton provides an option to replace traditional joint-based exoskeleton joints, which often have alignment issues with the biological joint. The proper fit of the exoskeleton to the user and task are research topics to reduce pain or joint injuries as well as for the execution of the task. The proposed task-based synthesis method was successfully applied to generate the 3D motions of the elbow flexion and extensions using a one degree of freedom (DOF), spatial four-bar mechanism. The elbow joint is analyzed through motion capture system to develop the bio-exoskeleton. The resulted exoskeleton does not need to align with the corresponding limb joint to generate the desired anatomical motion. 

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2. Integration of sEMG-Based Learning and Adaptive Fuzzy Sliding Mode Control for an Exoskeleton Assist-as-Needed Support System

   https://doi.org/10.3390/machines11070671  

   Delgado, Pablo; Gonzalez, Nathan; Yihun, Yimesker (July 2023, Machines)

   Andres Blanco-Ortega (Ed.)

   This paper presents an adaptive Fuzzy Sliding Mode Control approach for an Assist-as-Needed (AAN) strategy to achieve effective human–exoskeleton synergy. The proposed strategy employs an adaptive instance-based learning algorithm to estimate muscle effort, based on surface Electromyography (sEMG) signals. To determine and control the inverse dynamics of a highly nonlinear 4-degrees-of-freedom exoskeleton designed for upper-limb therapeutic exercises, a modified Recursive Newton-Euler Algorithm (RNEA) with Sliding Mode Control (SMC) was used. The exoskeleton position error and raw sEMG signal from the bicep’s brachii muscle were used as inputs for a fuzzy inference system to produce an output to adjust the sliding mode control law parameters. The proposed robust control law was simulated using MATLAB-Simulink, and the results showed that it could instantly adjust the necessary support, based on the combined motion of the human–exoskeleton system’s muscle engagement, while keeping the state trajectory errors and input torque bounded within ±5×10−2 rads and ±5 N.m, respectively. 

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3. Assessment of Task and Joint-Based Exoskeleton Designs for Elbow Joint Rehabilitation

   https://doi.org/10.1115/DMD2022-1034  

   Delgado, Pablo; Jaradat, Lieth; Yihun, Yimesker (April 2022, Frontiers in Biomedical Devices)

   Exoskeletons and robots have been used as a common practice to assist and automate rehabilitation exercises. Exoskeleton fitting and alignments are important factors and challenges that need to be addressed for smooth and safe operations and better outcomes. Such challenges often dictate the exoskeleton design approaches. Some focus on simplifying and mimicking human joints (joint-based) while others have a focus on a specific task (task-based), which does not need to align with the corresponding limb joint/s to generate the desired anatomical motion. In this study, the two design approaches are assessed in an elbow flexion-extension task. The muscle responses have been collected and compared with and without the exoskeletons. Based on 6 with no disability participants, the normalized Electromyography (EMG) RMS values are plotted. The plot profiles and magnitudes are used as a base to assess the exoskeleton alignment. For this specific task, the task-based exoskeleton has shown a profile closer to the one without exoskeleton with a relatively identical support as the joint-based one; the latter is evidenced through most subjects’ muscle response magnitudes. This preliminary data has shown a good methodology and insight towards the assessment of exoskeletons, but more human subject data is needed with different task combinations to further strengthen the findings. 

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4. Integration of EMG-Based Learning and Sliding Mode Control for an Exoskeleton Assist-as-Needed Support System

   https://doi.org/10.1115/DETC2022-91305  

   Delgado, Pablo; Gonzalez, Nathan; Yihun, Yimesker (August 2022, American Society of Mechanical Engineers)

   Abstract In this study, an electromyography (EMG) signal-based learning is integrated with a Sliding-Mode Control (SMC) law for an effective human-exoskeleton synergy. A modified Recursive Newton-Euler Algorithm (RNEA) with SMC was used to determine and control the inverse dynamics of a highly nonlinear 4 degree-of-freedom exoskeleton designed for the automation of upper-limp therapeutic exercises. The exoskeleton position and velocity, along with the raw EMG signal from the bicep Brachii muscle were used as a feedback. The root mean square (RMS) values of targeted muscles EMG are tracked in a predetermined time window to quantify an adaptive threshold value and control the torque at the exoskeleton joints accordingly. Simulations of the proposed robust control law have been done in MATLAB-Simulink. Results have shown that the designed hybrid Control strategy offers the ability to adjust the needed support instantly based on the amount of muscle engagement presented in the combined motion of the human-exoskeleton system while maintaining the state trajectory errors and input torque bounded to ±7 × 10−3 rads and ±5 N.m, respectively. 

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5. Characterizing the Sensing Response of Carbon Nanocomposite-Based Wearable Sensors on Elbow Joint Using an End Point Robot and Virtual Reality

   https://doi.org/10.3390/s24154894  

   Chaudhari, Amit; Lokesh, Rakshith; Chheang, Vuthea; Doshi, Sagar M; Barmaki, Roghayeh Leila; Cashaback, Joshua_G A; Thostenson, Erik T (August 2024, Sensors)

   Physical therapy is often essential for complete recovery after injury. However, a significant population of patients fail to adhere to prescribed exercise regimens. Lack of motivation and inconsistent in-person visits to physical therapy are major contributing factors to suboptimal exercise adherence, slowing the recovery process. With the advancement of virtual reality (VR), researchers have developed remote virtual rehabilitation systems with sensors such as inertial measurement units. A functional garment with an integrated wearable sensor can also be used for real-time sensory feedback in VR-based therapeutic exercise and offers affordable remote rehabilitation to patients. Sensors integrated into wearable garments offer the potential for a quantitative range of motion measurements during VR rehabilitation. In this research, we developed and validated a carbon nanocomposite-coated knit fabric-based sensor worn on a compression sleeve that can be integrated with upper-extremity virtual rehabilitation systems. The sensor was created by coating a commercially available weft knitted fabric consisting of polyester, nylon, and elastane fibers. A thin carbon nanotube composite coating applied to the fibers makes the fabric electrically conductive and functions as a piezoresistive sensor. The nanocomposite sensor, which is soft to the touch and breathable, demonstrated high sensitivity to stretching deformations, with an average gauge factor of ~35 in the warp direction of the fabric sensor. Multiple tests are performed with a Kinarm end point robot to validate the sensor for repeatable response with a change in elbow joint angle. A task was also created in a VR environment and replicated by the Kinarm. The wearable sensor can measure the change in elbow angle with more than 90% accuracy while performing these tasks, and the sensor shows a proportional resistance change with varying joint angles while performing different exercises. The potential use of wearable sensors in at-home virtual therapy/exercise was demonstrated using a Meta Quest 2 VR system with a virtual exercise program to show the potential for at-home measurements. 

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