Robot-Assisted Rehabilitation Architecture Supported by a Distributed Data Acquisition System

Self Cite

The accurate measurement of joint angles during patient rehabilitation is crucial for informed decision making by physiotherapists. Presently, visual inspection stands as one of the prevalent methods for angle assessment. Although it could appear the most straightforward way to assess the angles, it presents a problem related to the high susceptibility to error in the angle estimation. In light of this, this study investigates the possibility of using a new approach to angle calculation: a hybrid approach leveraging both a camera and LiDAR technology, merging image data with point cloud information. This method employs AI-driven techniques to identify the individual and their joints, utilizing the cloud-point data for angle computation. The tests, considering different exercises with different perspectives and distances, showed a slight improvement compared to using YOLO v7 for angle calculation. However, the improvement comes with higher system costs when compared with other image-based approaches due to the necessity of equipment such as LiDAR and a loss of fluidity during the exercise performance. Therefore, the cost–benefit of the proposed approach could be questionable. Nonetheless, the results hint at a promising field for further exploration and the potential viability of using the proposed methodology.

Section: Introductionmentioning

confidence: 99%

Angle Assessment for Upper Limb Rehabilitation: A Novel Light Detection and Ranging (LiDAR)-Based Approach

Klein,

Chellal,

Grilo

et al. 2024

Self Cite

“…The use of robotics in neurorehabilitation is not a new concept (as it was already proposed at the beginning of the previous century [ 12 ]) and many of them are nowadays used in standard rehabilitation practice [ 13 , 14 , 15 , 16 , 17 ]. Such devices combine organically with the concepts of neuroplasticity and serious gaming, resulting in numerous successful examples of application both for the upper and the lower limbs [ 18 , 19 ] and for diseases spanning from trauma to muscular dystrophies and even mental disorders [ 1 ].…”

Section: Introductionmentioning

confidence: 99%

NeuroSuitUp: System Architecture and Validation of a Motor Rehabilitation Wearable Robotics and Serious Game Platform

Mitsopoulos

Fiska

Tagaras

et al. 2023

Background: This article presents the system architecture and validation of the NeuroSuitUp body–machine interface (BMI). The platform consists of wearable robotics jacket and gloves in combination with a serious game application for self-paced neurorehabilitation in spinal cord injury and chronic stroke. Methods: The wearable robotics implement a sensor layer, to approximate kinematic chain segment orientation, and an actuation layer. Sensors consist of commercial magnetic, angular rate and gravity (MARG), surface electromyography (sEMG), and flex sensors, while actuation is achieved through electrical muscle stimulation (EMS) and pneumatic actuators. On-board electronics connect to a Robot Operating System environment-based parser/controller and to a Unity-based live avatar representation game. BMI subsystems validation was performed using exercises through a Stereoscopic camera Computer Vision approach for the jacket and through multiple grip activities for the glove. Ten healthy subjects participated in system validation trials, performing three arm and three hand exercises (each 10 motor task trials) and completing user experience questionnaires. Results: Acceptable correlation was observed in 23/30 arm exercises performed with the jacket. No significant differences in glove sensor data during actuation state were observed. No difficulty to use, discomfort, or negative robotics perception were reported. Conclusions: Subsequent design improvements will implement additional absolute orientation sensors, MARG/EMG based biofeedback to the game, improved immersion through Augmented Reality and improvements towards system robustness.

“…While exoskeletons provide direct contact on a human for rehabilitation and assistance, other medical devices with similar hardware can assist in a variety of other tasks. These medical devices include teleoperation for robotic surgery with haptic feedback [ 26 ], assistive rehabilitation [ 27 ], emotional and mental care [ 28 ], robot-assisted surgery with speech-based communication [ 29 ], and even training simulators for experienced physicians in assessing and evaluating ankle clonus, a set of involuntary and rhythmic muscle spasms [ 30 ]. Additionally, humanoid robots are commonly driven using similar sensor feedback and actuation approaches to exoskeletons and other medical devices.…”

Section: Introductionmentioning

confidence: 99%

“…These control strategies are often paired with finite state machines to determine the intended motion of the user, and employ gait trajectory planners to achieve a whole-body behavior [ 11 , 13 , 14 , 15 ]. These algorithms are heavily dependent on the quality of the sensor feedback, utilizing encoders to measure joint and actuation positions [ 14 , 16 , 17 ], linear spring encoders [ 17 , 27 ], and force–torque sensors [ 12 , 13 , 14 , 16 ]. This high-level controller is often on board an external PC, where the computation capability is higher and completely dedicated to solving complex dynamic equations and optimization problems as quickly as possible [ 13 , 16 ].…”

Section: Introductionmentioning

confidence: 99%

“…As displayed in Figure 1 , the LSEA (green) contains several different sources of sensor feedback: motor current, quadrature and absolute encoder, and force sensor feedback. The LSEA mechanism here utilizes an inline force sensor [ 20 ] instead of a linear encoder to measure elastic spring deflection [ 17 , 27 ]. Finally, a low-level controller can collect sensor feedback from a single inertial measurement unit (IMU) while communicating and controlling two LSEAs.…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Design and Validation of a Low-Level Controller for Hierarchically Controlled Exoskeletons

Herron

Fuge

Kogelis

et al. 2023

In this work, a generalized low-level controller is presented for sensor collection, motor input, and networking with a high-level controller. In hierarchically controlled exoskeletal systems, which utilize series elastic actuators (SEAs), the hardware for sensor collection and motor command is separated from the computationally expensive high-level controller algorithm. The low-level controller is a hardware device that must collect sensor feedback, condition and filter the measurements, send actuator inputs, and network with the high-level controller at a real-time rate. This research outlines the hardware of two printed circuit board (PCB) designs for collecting and conditioning sensor feedback from two SEA subsystems and an inertial measurement unit (IMU). The SEAs have a joint and motor encoder, motor current, and force sensor feedback that can be measured using the proposed generalized low-level controller presented in this work. In addition, the high and low-level networking approach is discussed in detail, with a full breakdown of the data storage within a communication frame during the run-time operation. The challenges of device synchronization and updates rates of high and low-level controllers are also discussed. Further, the low-level controller was validated using a pendulum test bed, complete with full sensor feedback, including IMU results for two open-loop scenarios. Moreover, this work can be extended to other hierarchically controlled robotic systems that utilize SEA subsystems, such as humanoid robots, assistive rehabilitation robots, training simulators, and robotic-assisted surgical devices. The hardware and software designs presented in this work are available open source to enable researchers with a direct solution for data acquisition and the control of low-level devices in a robotic system.