Upper Limb Home-Based Robotic Rehabilitation During COVID-19 Outbreak.

The coronavirus disease (COVID-19) outbreak requires rapid reshaping of rehabilitation services to include patients recovering from severe COVID-19 with post-intensive care syndromes, which results in physical deconditioning and cognitive impairments, patients with comorbid conditions, and other patients requiring physical therapy during the outbreak with no or limited access to hospital and rehabilitation centers. Considering the access barriers to quality rehabilitation settings and services imposed by social distancing and stay-at-home orders, these patients can be benefited from providing access to affordable and good quality care through home-based rehabilitation. The success of such treatment will depend highly on the intensity of the therapy and effort invested by the patient. Monitoring patients' compliance and designing a home-based rehabilitation that can mentally engage them are the critical elements in home-based therapy's success. Hence, we study the state-of-the-art telerehabilitation frameworks and robotic devices, and comment about a hybrid model that can use existing telerehabilitation framework and home-based robotic devices for treatment and simultaneously assess patient's progress remotely. Second, we comment on the patients' social support and engagement, which is critical for the success of telerehabilitation service. As the therapists are not physically present to guide the patients, we also discuss the adaptability requirement of home-based telerehabilitation. Finally, we suggest that the reformed rehabilitation services should consider both home-based solutions for enhancing the activities of daily living and an on-demand ambulatory rehabilitation unit for extensive training where we can monitor both cognitive and motor performance of the patients remotely.

Improving Stability in Upper Limb Rehabilitation Using Variable Stiffness.

In a robotic rehabilitation setup, patient's safety and interaction stability are critical throughout the therapy. This paper addresses the stability aspect by proposing a method to vary the endpoint stiffness using a variable impedance mechanism. The proposed device consists of permanent magnets in an antagonistic configuration that acts as springs and the variation in stiffness is achieved by modifying the separation between those magnets. This device is mounted on the end-effector of an admittance controlled robotic arm and tested with the help of healthy humans on a virtual maze traversal experiment consisting of both fine and gross motor regions. Moreover, the subjects are tested both in normal and simulated tremor conditions to verify the effectiveness of the device. The experimental results show that the VSM can not only suppress the high-frequency forces but can also reduce the interference of human endpoint stiffness in the stability of the robot.

Surgical Skill Assessment using Motor Control Features and Hidden Markov Model.

Surgical Skill Assessment has increased interest through which the training and objective feedback to surgeons can be given based on the task performance. In this paper, motor control features which are a part of psychomotor learning, are developed based on the camera plane coordinates of the tip of the tools from the videos of surgeons performing the Urethro-Vesicle Anastomosis (UVA) surgical task. Classification into Novices (N) and Experts (E), when compared to the manual encoding of subject expertise based on the Dreyfus model, resulted in high accuracy. Additionally, this study could form a basis for closed loop surgical training, specifically for the novitiate surgeons.

A machine learning approach to detect changes in gait parameters following a fatiguing occupational task.

The purpose of this study is to provide a method for classifying non-fatigued vs. fatigued states following manual material handling. A method of template matching pattern recognition for feature extraction ($1 Recognizer) along with the support vector machine model for classification were applied on the kinematics of gait cycles segmented by our stepwise search-based segmentation algorithm. A single inertial measurement unit on the ankle was used, providing a minimally intrusive and inexpensive tool for monitoring. The classifier distinguished between states using distance-based scores from the recogniser and the step duration. The results of fatigue detection showed an accuracy of 90% across data from 20 recruited subjects. This method utilises the minimum amount of data and features from only one low-cost sensor to reliably classify the state of fatigue induced by a realistic manufacturing task using a simple machine learning algorithm that can be extended to real-time fatigue monitoring as a future technology to be employed in the manufacturing facilities. Practitioner Summary: We examined the use of a wearable sensor for the detection of fatigue-related changes in gait based on a simulated manual material handling task. Classification based on foot acceleration and position trajectories resulted in 90% accuracy. This method provides a practical framework for predicting realistic levels of fatigue.

Quantitative estimation of electro-osmosis force on charged particles inside a borosilicate resistive-pulse sensor.

Nano and micron-scale pore sensors have been widely used for biomolecular sensing application due to its sensitive, label-free and potentially cost-effective criteria. Electrophoretic and electroosmosis are major forces which play significant roles on the sensor's performance. In this work, we have developed a mathematical model based on experimental and simulation results of negatively charged particles passing through a 2µm diameter solid-state borosilicate pore under a constant applied electric field. The mathematical model has estimated the ratio of electroosmosis force to electrophoretic force on particles to be 77.5%.

Understanding Cognitive Performance During Robot-Assisted Surgery.

OBJECTIVE: To understand cognitive function of an expert surgeon in various surgical scenarios while performing robot-assisted surgery. MATERIALS AND METHODS: In an Internal Review Board approved study, National Aeronautics and Space Administration-Task Load Index (NASA-TLX) questionnaire with surgical field notes were simultaneously completed. A wireless electroencephalography (EEG) headset was used to monitor brain activity during all procedures. Three key portions were evaluated: lysis of adhesions, extended lymph node dissection, and urethro-vesical anastomosis (UVA). Cognitive metrics extracted were distraction, mental workload, and mental state. RESULTS: In evaluating lysis of adhesions, mental state (EEG) was associated with better performance (NASA-TLX). Utilizing more mental resources resulted in better performance as self-reported. Outcomes of lysis were highly dependent on cognitive function and decision-making skills. In evaluating extended lymph node dissection, there was a negative correlation between distraction level (EEG) and mental demand, physical demand and effort (NASA-TLX). Similar to lysis of adhesion, utilizing more mental resources resulted in better performance (NASA-TLX). Lastly, with UVA, workload (EEG) negatively correlated with mental and temporal demand and was associated with better performance (NASA-TLX). The EEG recorded workload as seen here was a combination of both cognitive performance (finding solution) and motor workload (execution). Majority of workload was contributed by motor workload of an expert surgeon. During UVA, muscle memory and motor skills of expert are keys to completing the UVA. CONCLUSION: Cognitive analysis shows that expert surgeons utilized different mental resources based on their need.

Cognitive skills assessment during robot-assisted surgery: separating the wheat from the chaff.

OBJECTIVE: To investigate the utility of cognitive assessment during robot-assisted surgery (RAS) to define skills in terms of cognitive engagement, mental workload, and mental state; while objectively differentiating between novice and expert surgeons. SUBJECTS AND METHODS: In all, 10 surgeons with varying operative experience were assigned to beginner (BG), combined competent and proficient (CPG), and expert (EG) groups based on the Dreyfus model. The participants performed tasks for basic, intermediate and advanced skills on the da Vinci Surgical System. Participant performance was assessed using both tool-based and cognitive metrics. RESULTS: Tool-based metrics showed significant differences between the BG vs CPG and the BG vs EG, in basic skills. While performing intermediate skills, there were significant differences only on the instrument-to-instrument collisions between the BG vs CPG (2.0 vs 0.2, P = 0.028), and the BG vs EG (2.0 vs 0.1, P = 0.018). There were no significant differences between the CPG and EG for both basic and intermediate skills. However, using cognitive metrics, there were significant differences between all groups for the basic and intermediate skills. In advanced skills, there were no significant differences between the CPG and the EG except time (1116 vs 599.6 s), using tool-based metrics. However, cognitive metrics revealed significant differences between both groups. CONCLUSION: Cognitive assessment of surgeons may aid in defining levels of expertise performing complex surgical tasks once competence is achieved. Cognitive assessment may be used as an adjunct to the traditional methods for skill assessment during RAS.

Foot-mounted inertial measurement unit for activity classification.

This paper proposes a classification technique for daily base activity recognition for human monitoring during physical therapy in home. The proposed method estimates the foot motion using single inertial measurement unit, then segments the motion into steps classify them by template-matching as walking, stairs up or stairs down steps. The results show a high accuracy of activity recognition. Unlike previous works which are limited to activity recognition, the proposed approach is more qualitative by providing similarity index of any activity to its desired template which can be used to assess subjects improvement.