A Case Study of Upper Limb Robotic-Assisted Therapy Using the Track-Hold Device

Impact of a Dynamic Orthosis on Manual Dexterity Among People With Parkinson's Disease: A Randomized Trial

IMPORTANCE

A dynamic elastomeric fabric orthosis could be a novel nonpharmacological treatment of motor symptoms among people with Parkinson's disease (PD).

OBJECTIVE

To evaluate the efficacy of the orthosis in improving manual dexterity among people with PD.

DESIGN

A randomized trial with 60 participants (control group, n = 20; experimental group, n = 40). Manual dexterity was assessed in on-off states of the disease, with and without the orthosis.

SETTING

Burgos University Hospital (Burgos, Spain).

PARTICIPANTS

Consecutive nonprobabilistic sampling. Patients were included if they had been diagnosed with PD, had motor symptoms in at least one upper limb, and attended the hospital's neurology department. Participants were ages 48 to 89 yr, with an average disease duration of 5.38 yr (SD = 4.23). Exclusion criteria were tremor due to another neurological disease or a Montreal Cognitive Assessment score ≤26.

INTERVENTION

The experimental group used the orthosis on the most affected upper limb for 2 mo; the control group participants did not receive the orthosis.

OUTCOMES AND MEASURES

Manual dexterity was measured with the Purdue Pegboard Test, Minnesota Manual Dexterity Test, and Square Test. Paired t tests for related samples and analysis of covariance tests were used.

RESULTS

Some participants improved on some manual dexterity items while wearing the orthosis. However, it was not sustained when the orthosis was removed after 2 mo of use.

CONCLUSIONS AND RELEVANCE

The orthosis may improve certain aspects of manual dexterity and functionality among people with PD, but only while it is worn. Plain-Language Summary: The common motor symptoms and declines in manual dexterity that people with Parkinson's disease (PD) experience can significantly affect their ability to perform activities of daily living as well as their quality of life. Manual dexterity is a strong predictor of the level of functional independence for people with PD. This study evaluated the efficacy of an elastomeric fabric orthosis to improve manual dexterity for people with PD. The study found that while wearing the orthosis the motor symptoms of PD were reduced and manual dexterity and upper limb functionality improved. Wearing the orthosis has the potential to enhance functionality, foster greater autonomy, and substantially improve the quality of life of people with PD.

Eye-in-Hand Robotic Arm Gripping System Based on Machine Learning and State Delay Optimization

This research focused on using RGB-D images and modifying an existing machine learning network architecture to generate predictions of the location of successfully grasped objects and to optimize the control system for state delays. A five-finger gripper designed to mimic the human palm was tested to demonstrate that it can perform more delicate missions than many two- or three-finger grippers. Experiments were conducted using the 6-DOF robot arm with the five-finger and two-finger grippers to perform at least 100 actual machine grasps, and compared to the results of other studies. Additionally, we investigated state time delays and proposed a control method for a robot manipulator. Many studies on time-delay systems have been conducted, but most focus on input and output delays. One reason for this emphasis is that input and output delays are the most commonly occurring delays in physical or electronic systems. An additional reason is that state delays increase the complexity of the overall control system. Finally, it was demonstrated that our network can perform as well as a deep network architecture with little training data and omitting steps, such as posture evaluation, and when combined with the hardware advantages of the five-finger gripper, it can produce an automated system with a gripping success rate of over 90%. This paper is an extended study of the conference paper.

Using Artificial Neuro-Molecular System in Robotic Arm Motion Control-Taking Simulation of Rehabilitation as an Example

Under the delicate control of the brain, people can perform graceful movements through the coordination of muscles, bones, ligaments, and joints. If artificial intelligence can be used to establish a control system that simulates the movements of human arms, it is believed that the application scope of robotic arms in assisting people's daily life can be greatly increased. The purpose of this study is to build a general system that can use intelligent techniques to assist in the construction of a personalized rehabilitation system. More importantly, this research hopes to establish an intelligent system that can be adjusted according to the needs of the problem domain, that is, the system can move toward the direction of problem-solving through autonomous learning. The artificial neural molecular system (ANM system), developed early in our laboratory, which captured the close structure/function relationship of biological systems, was used. The system was operated on the V-REP (Virtual Robot Experimentation Platform). The results show that the ANM system can use self-learning methods to adjust the start-up time, rotation angle, and the sequence of the motor operation of different motors in order to complete the designated task assignment.