A Digital Twin for Exoskeleton Pilot

NCT ID: NCT05650255

Last Updated: 2025-03-20

Basic Information

• Recruitment Status: RECRUITING
• Clinical Phase: NA
• Total Enrollment: 30 participants
• Study Classification: INTERVENTIONAL
• Study Start Date: 2022-11-07
• Study Completion Date: 2025-10-31

Brief Summary

Although exoskeletons have been studied for many years, actual products have been sold on the market. However, there are still many limitations in the practical application of exoskeletons, and the control logic is also limited to the level of motor torque compensation, and lacks the consideration of overall motion stability (such as whether the wearer (driving, pilot) will fall). The purpose of this project is to establish a digital twin of this driving, which will perform the driving action in parallel, and calculate in parallel in the virtual environment the torque required to provide each joint torque to maintain stable walking without falling, and this torque can be used as a monitoring basis for the auxiliary torque of each axis for the exoskeleton. When the exoskeleton's movements are out of range of a stable gait, the controller can warn and attenuate the output of auxiliary forces, and on the other hand, it has the opportunity to analyze whether the intention of driving is to switch the movement mode - such as stopping, or sitting, squatting, etc.

Detailed Description

This project is expected to use different machine learning models to make accurate predictions of human intent when healthy people drive different actions or action transitions of common human actions. Drivers will wear sensors such as inertial measurement unit (IMU) and electromyography (EMG) in the lower limbs to measure body signals of participants during actions in a non-invasive way, and let the system calculate the joint angle, angular velocity and angular acceleration of each joint of the driving lower limb related human signals.

Conditions

Human Intention; Exoskeletons

Study Design

• Allocation Method: NA
• Intervention Model: SINGLE_GROUP
• Primary Study Purpose: BASIC_SCIENCE
• Blinding Strategy: NONE

Study Groups

A Digital Twin for Exoskeleton Pilot

This project is expected to use different machine learning models to make accurate predictions of human intent when healthy people drive different actions or action transitions of common human actions. Drivers will wear sensors such as inertial measurement unit (IMU) and electromyography (EMG) in the lower limbs to measure body signals of participants during actions in a non-invasive way, and let the system calculate the joint angle, angular velocity and angular acceleration of each joint of the driving lower limb related human signals.

Group Type: EXPERIMENTAL

A Digital Twin for Exoskeleton Pilot

Intervention Type: OTHER

The purpose of this project is to establish a digital twin of this driving, which will perform the driving action in parallel, and calculate in parallel in the virtual environment the torque required to provide each joint torque to maintain stable walking without falling, and this torque can be used as a monitoring basis for the auxiliary torque of each axis for the exoskeleton. When the exoskeleton's movements are out of range of a stable gait, the controller can warn and attenuate the output of auxiliary forces, and on the other hand, it has the opportunity to analyze whether the intention of driving is to switch the movement mode - such as stopping, or sitting, squatting, etc.

Interventions

A Digital Twin for Exoskeleton Pilot

The purpose of this project is to establish a digital twin of this driving, which will perform the driving action in parallel, and calculate in parallel in the virtual environment the torque required to provide each joint torque to maintain stable walking without falling, and this torque can be used as a monitoring basis for the auxiliary torque of each axis for the exoskeleton. When the exoskeleton's movements are out of range of a stable gait, the controller can warn and attenuate the output of auxiliary forces, and on the other hand, it has the opportunity to analyze whether the intention of driving is to switch the movement mode - such as stopping, or sitting, squatting, etc.

Intervention Type: OTHER

Eligibility Criteria

Inclusion Criteria

1. Can stand independently and walk for more than 5 minutes.

2. According to age, it can be divided into: elderly groups (aged between 50~80 years old); Young people (aged 18~50 years).

Exclusion Criteria

1. Have current or previous neurological diseases of the lower extremities, such as: acute neuritis, stroke, Parkinson's disease.

2. Have current or previous orthopedic diseases of the lower extremities, such as: knee replacement, rheumatoid arthritis, or sports injuries and other symptoms.

• Minimum Eligible Age: 18 Years
• Maximum Eligible Age: 80 Years
• Eligible Sex: ALL
• Accepts Healthy Volunteers: Yes

Sponsors

National Taiwan University Hospital

OTHER

Sponsor Role: lead

Responsible Party

Responsibility Role: SPONSOR

Principal Investigators

Wei-Li Hsu, Ph.D

Role: STUDY_DIRECTOR

Study Principal Investigator National Taiwan Unversity

Locations

National Taiwan University Hospital

Taipei,Taiwan, , Taiwan

Site Status: RECRUITING

Countries

Taiwan

Central Contacts

Wei-Li Hsu, Ph.D

Role: CONTACT

wlhsu@ntu.edu.tw

+886-2-3366-8127

Facility Contacts

Wei-Li Hsu, Ph.D

Role: primary

wlhsu@ntu.edu.tw

Other Identifiers

202209051RINB

Identifier Type: -

Identifier Source: org_study_id