Design and Evaluation of a Compact 3D End-effector Assistive Robot for Adaptive Arm Support

Основні поняття

The proposed Assistive Robotic Arm Extender (ARAE) system provides transparency in 3D movement and adaptive arm support control to enable effective training with Activities of Daily Living (ADLs) and interaction with real environments.

Анотація

The paper introduces the Assistive Robotic Arm Extender (ARAE), a 3D end-effector type of upper limb assistive robot. The key highlights are:

1. Mechanical Design: The ARAE has 5 degrees of freedom, including 3 active motors and 2 passive joints, based on a parallel mechanism design. It uses quasi-direct drive motors to achieve high transparency and backdrivability.

2. Adaptive Arm Support Control Framework:

   • Human Joint Angle Estimation: Two methods are proposed - fixed torso model and sagittal plane model - to estimate human arm joint angles without using external sensors.
   • Arm Gravity Compensation: The estimated joint angles are used in a human arm dynamics model to calculate the required support force at the end-effector.

3. Experimental Evaluation:

   • The two joint angle estimation methods were validated against motion capture data. The sagittal plane model showed better performance, especially during torso movements.
   • The effects of the adaptive arm support control were evaluated by measuring muscle activities. Significant reductions in muscle activation were observed when using the ARAE system compared to no robot assistance.

The ARAE system, combined with the proposed adaptive arm support control framework, has the potential to enable effective training with ADLs and interaction with real environments for patients with upper limb impairments.

Статистика

The mean mass of the participants is 75.05 ± 2.5kg and the mean height is 178 ± 4.23cm.
The mean upper limb length (lU) is 29.91±0.25cm and the forearm length (lF) is 26.43 ± 0.66cm.
The mean of trunk length (lSH) is 38.50 ± 1.04cm and the mean of trunk width (lPH) is 17.93 ± 0.64cm.

Цитати

"The ARAE system, when combined with the proposed control framework, has the potential to offer adaptive arm support. This integration could enable effective training with Activities of Daily Living (ADLs) and interaction with real environments."
"The sagittal plane model is more suitable for estimating joint angles during torso movements. When the shoulder undergoes significant movements in the sagittal plane, the accuracy of both models decreases as the torso moves forward. However, the sagittal plane model significantly improves the angle estimation accuracy compared to the fixed torso model."

Ключові висновки, отримані з

Design and Evaluation of a Compact 3D End-effector Assistive Robot for Adaptive Arm Support

by Sibo Yang,Li... о arxiv.org 04-05-2024

https://arxiv.org/pdf/2404.03149.pdf

Глибші Запити

How can the personalized model be developed to further improve the adaptability of the ARAE system to different users and tasks?

To enhance the adaptability of the ARAE system, the personalized model can be developed by incorporating individual-specific parameters and characteristics. This can involve collecting data on the anthropometric measurements, range of motion, muscle strength, and any specific limitations or requirements of each user. By creating a personalized model for each user, the ARAE system can tailor the support and assistance provided based on the unique needs and capabilities of the individual. Additionally, machine learning algorithms can be utilized to continuously adapt and optimize the personalized model based on user feedback and performance data. This iterative process can lead to a more precise and effective rehabilitation experience for each user.

What are the potential challenges in conducting patient trials to evaluate the usability and performance of the ARAE system for rehabilitation applications?

Conducting patient trials to evaluate the usability and performance of the ARAE system for rehabilitation applications can present several challenges. One major challenge is ensuring the safety and comfort of the patients during the trials, especially if they have mobility impairments or specific medical conditions. Additionally, recruiting a diverse range of participants with varying levels of impairment and rehabilitation needs can be challenging. It is essential to consider ethical considerations, such as informed consent, privacy, and data security, when involving patients in trials. Furthermore, obtaining regulatory approvals and navigating the healthcare system to conduct trials in clinical settings can be time-consuming and complex. Ensuring proper training and support for both patients and healthcare providers using the ARAE system is crucial for the success of the trials.

How can the adaptive arm support control framework be extended to incorporate other modalities, such as electromyography or brain-computer interfaces, to provide a more comprehensive and personalized rehabilitation solution?

Integrating electromyography (EMG) or brain-computer interfaces (BCIs) into the adaptive arm support control framework can enhance the personalized rehabilitation solution offered by the ARAE system. EMG can be used to monitor muscle activity and provide real-time feedback on the user's muscle engagement and fatigue levels. By incorporating EMG data into the control framework, the system can adjust the support force and assistance provided based on the user's muscle activity patterns. Similarly, BCIs can enable direct communication between the user's brain signals and the robotic system, allowing for more intuitive and natural control of the ARAE. This integration can enable users to control the robot using their brain signals, enhancing the user experience and personalization of the rehabilitation process. By combining these modalities, the ARAE system can offer a more comprehensive and adaptive solution for upper limb rehabilitation.