Automating provision of feedback to stroke patients with and without information on compensatory movements: A pilot study

Socially Assistive Robot for Stroke Rehabilitation: A Long-Term in-the-Wild Pilot Randomized Controlled Trial

Socially assistive robots (SARs) have been suggested as a platform for post-stroke training. It is not yet known whether long-term interaction with a SAR can lead to an improvement in the functional ability of individuals post-stroke. The aim of this pilot study was to compare the changes in motor ability and quality of life following a long-term intervention for upper-limb rehabilitation of post-stroke individuals using three approaches: 1) training with a SAR in addition to usual care; 2) training with a computer in addition to usual care; and 3) usual care with no additional intervention. Thirty-three post-stroke patients with moderate-severe to mild impairment were randomly allocated into three groups: two intervention groups - one with a SAR (ROBOT group) and one with a computer (COMPUTER group) - and one control group with no intervention (CONTROL group). The intervention sessions took place three times/week, for a total of 15 sessions/participant; The study was conducted over a period of two years, during which 306 sessions were held. Twenty-six participants completed the study. Participants in the ROBOT group significantly improved in their kinematic and clinical measures which included smoothness of movement, action research arm test (ARAT), and Fugl-Meyer upper-extremity assessment (FMA-UE). No significant improvement in these measures was found in the COMPUTER or the control groups. 100% of the participants in the SAR group gained improvement which reached - or exceeded - the minimal clinically important difference in the ARAT, the gold standard for upper-extremity activity performance post-stroke. This study demonstrates both the feasibility and the clinical benefit of using a SAR for long-term interaction with post-stroke individuals as part of their rehabilitation program. Trial Registration: ClinicalTrials.gov NCT03651063.

Online detection of compensatory strategies in human movement with supervised classification: a pilot study

Introduction

Stroke survivors often compensate for the loss of motor function in their distal joints by altered use of more proximal joints and body segments. Since this can be detrimental to the rehabilitation process in the long-term, it is imperative that such movements are indicated to the patients and their caregiver. This is a difficult task since compensation strategies are varied and multi-faceted. Recent works that have focused on supervised machine learning methods for compensation detection often require a large training dataset of motions with compensation location annotations for each time-step of the recorded motion. In contrast, this study proposed a novel approach that learned a linear classifier from energy-based features to discriminate between healthy and compensatory movements and identify the compensating joints without the need for dense and explicit annotations.

Methods

Six healthy physiotherapists performed five different tasks using healthy movements and acted compensations. The resulting motion capture data was transformed into joint kinematic and dynamic trajectories. Inspired by works in bio-mechanics, energy-based features were extracted from this dataset. Support vector machine (SVM) and logistic regression (LR) algorithms were then applied for detection of compensatory movements. For compensating joint identification, an additional condition enforcing the independence of the feature calculation for each observable degree of freedom was imposed.

Results

Using leave-one-out cross validation, low values of mean brier score (<0.15), mis-classification rate (<0.2) and false discovery rate (<0.2) were obtained for both SVM and LR classifiers. These methods were found to outperform deep learning classifiers that did not use energy-based features. Additionally, online classification performance by our methods were also shown to outperform deep learning baselines. Furthermore, qualitative results obtained from the compensation joint identification experiment indicated that the method could successfully identify compensating joints.

Discussion

Results from this study indicated that including prior bio-mechanical information in the form of energy based features can improve classification performance even when linear classifiers are used, both for offline and online classification. Furthermore, evaluation compensation joint identification algorithm indicated that it could potentially provide a straightforward and interpretable way of identifying compensating joints, as well as the degree of compensation being performed.

Socially Assistive Robots for Parkinson's Disease: Needs, Attitudes and Specific Applications as Identified by Healthcare Professionals

To explore how socially assistive robots (SARs) may assist the specific needs of individuals with Parkinson's disease (IwPD), we conducted three focus groups with 12 clinicians who treat IwPD. We present a thematic analysis of their perceptions of the needs of the patients, and their own expectations, perceived advantages, disadvantages and concerns regarding the use of SARs for IwPD. Clinicians were positive towards using SARs for IwPD, if used in the patient's home, for motor, communication, emotional, and cognitive needs, especially for practice and for help with activities of daily living. They were concerned that a SAR might be used to replace clinicians’ work, and stressed it should only augment the clinicians’ work. They thought a SAR may relieve some of the burden experienced by informal caregivers, and identified specific applications for SARs for PD. We asked 18 stakeholders (nine IwPD, nine family members) to rate their level of agreement with the clinicians’ statements. The greatest divergence between their views and those of the clinicians was on the topic of using a SAR as a companion, or as a feeding assistant, to which they objected. This work may be used as a basis for future studies designing SARs for IwPD.