Real-time Estimation of the Strength Capacity of the Upper Limb for Physical Human-Robot Collaboration.

In physical Human-Robot Collaboration (pHRC), having an estimate of the operator's strength capacity can help implement control strategies. Currently, the trend is to integrate devices that can measure physiological signals. This is not always a viable option, especially for highly dynamic tasks.

Upper limb strength estimation of physically impaired persons using a musculoskeletal model: A sensitivity analysis.

Sensitivity of upper limb strength calculated from a musculoskeletal model was analyzed, with focus on how the sensitivity is affected when the model is adapted to represent a person with physical impairment. Sensitivity was calculated with respect to four muscle-tendon parameters: muscle peak isometric force, muscle optimal length, muscle pennation, and tendon slack length. Results obtained from a musculoskeletal model of average strength showed highest sensitivity to tendon slack length, followed by muscle optimal length and peak isometric force, which is consistent with existing studies.

A multi-stage design framework for the development of task-specific robotic exoskeletons.

This work presents a multi-stage design framework for developing robotic exoskeletons suited for specific tasks, such as individualized exercises that meet the needs of patients undergoing physical therapy. The framework systematically develops the exoskeleton based on the required task space, represented by a set of limb poses which may be defined directly, or indirectly using means such as motion capture. The design process seeks to maximize the poses inside and surrounding the defined task space whilst ensuring additional criteria required to perform the task are satisfied.

Admittance control scheme for implementing model-based assistance-as-needed on a robot.

A model-based assistance-as-needed paradigm has been developed to govern the assistance provided by an assistive robot to its operator. This paradigm has advantages over existing methods of providing assistance-as-needed for applications such as robotic rehabilitation. However, implementation of the model-based paradigm requires a control scheme to be developed which controls the robot so as to provide the assistance calculated by the model-based paradigm to its operator.

Experimental evaluation of a model-based assistance-as-needed paradigm using an assistive robot.

In robotic rehabilitation a promising paradigm is assistance-as-needed. This is because it promotes patient active participation which is essential for neuro-rehabilitation. A model-based assistance-as-needed paradigm has been developed which utilizes a musculoskeletal model representing the subject to calculate their assistance needs.

Estimating physical assistance need using a musculoskeletal model.

Technologies that provide physical assistance during tasks are often required to provide assistance specific to the task and person performing it. An example is robotic rehabilitation in which the assistance-as-needed (AAN) paradigm aims to provide operators with the minimum assistance required to perform the task. Current approaches use empirical performance-based methods which require repeated observation of the specific task before an estimate of the needed assistance can be determined.

A task description model for robotic rehabilitation.

The desire to produce robots to aid in physical neurorehabilitation has led to the control paradigm Assistance-As-Needed. This paradigm aims to assist patients in performing physical rehabilitation tasks whilst providing the least amount of assistance required, maximizing the patient's effort which is essential for recovery. Ideally the provided assistance equals the gap between the capability required to perform the task and the patient's available capability.

Towards using musculoskeletal models for intelligent control of physically assistive robots.

With the increasing number of robots being developed to physically assist humans in tasks such as rehabilitation and assistive living, more intelligent and personalized control systems are desired. In this paper we propose the use of a musculoskeletal model to estimate the strength of the user, from which information can be utilized to improve control schemes in which robots physically assist humans. An optimization model is developed utilizing a musculoskeletal model to estimate human strength in a specified dynamic state.