Smoothness metrics for reaching performance after stroke:Part 1: which one to choose?

BACKGROUND: Smoothness is commonly used for measuring movement quality of the upper paretic limb during reaching tasks after stroke. Many different smoothness metrics have been used in stroke research, but a ‘valid’ metric has not been identified. A systematic review and subsequent rigorous analysis of smoothness metrics used in stroke research, in terms of their mathematical definitions and response to simulated perturbations, is needed to conclude whether they are valid for measuring smoothness. Our objective was to provide a recommendation for metrics that reflect smoothness after stroke based on: (1) a systematic review of smoothness metrics for reaching used in stroke research, (2) the mathematical description of the metrics, and (3) the response of metrics to simulated changes associated with smoothness deficits in the reaching profile. METHODS: The systematic review was performed by screening electronic databases using combined keyword groups Stroke, Reaching and Smoothness. Subsequently, each metric identified was assessed with mathematical criteria regarding smoothness: (a) being dimensionless, (b) being reproducible, (c) being based on rate of change of position, and (d) not being a linear transform of other smoothness metrics. The resulting metrics were tested for their response to simulated changes in reaching using models of velocity profiles with varying reaching distances and durations, harmonic disturbances, noise, and sub-movements. Two reaching tasks were simulated; reach-to-point and reach-to-grasp. The metrics that responded as expected in all simulation analyses were considered to be valid. RESULTS: The systematic review identified 32 different smoothness metrics, 17 of which were excluded based on mathematical criteria, and 13 more as they did not respond as expected in all simulation analyses. Eventually, we found that, for reach-to-point and reach-to-grasp movements, only Spectral Arc Length (SPARC) was found to be a valid metric. CONCLUSIONS: Based on this systematic review and simulation analyses, we recommend the use of SPARC as a valid smoothness metric in both reach-to-point and reach-to-grasp tasks of the upper limb after stroke. However, further research is needed to understand the time course of smoothness measured with SPARC for the upper limb early post stroke, preferably in longitudinal studies. SUPPLEMENTARY INFORMATION: The online version contains supplementary material available at 10.1186/s12984-021-00949-6

Quantitative assessment based on kinematic measures of functional impairments during upper extremity movements: a review

Quantitative measures of human movement quality are important for discriminating healthy and pathological conditions and for expressing the outcomes and clinically important changes in subjects' functional state. However the most frequently used instruments for the upper extremity functional assessment are clinical scales, that previously have been standardized and validated, but have a high subjective component depending on the observer who scores the test. But they are not enough to assess motor strategies used during movements, and their use in combination with other more objective measures is necessary. The objective of the present review is to provide an overview on objective metrics found in literature with the aim of quantifying the upper extremity performance during functional tasks, regardless of the equipment or system used for registering kinematic data

Robotic and clinical evaluation of upper limb motor performance in patients with Friedreich's Ataxia: an observational study

Background: Friedreich’s ataxia (FRDA) is the most common hereditary autosomal recessive form of ataxia. In this disease there is early manifestation of gait ataxia, and dysmetria of the arms and legs which causes impairment in daily activities that require fine manual dexterity. To date there is no cure for this disease. Some novel therapeutic approaches are ongoing in different steps of clinical trial. Development of sensitive outcome measures is crucial to prove therapeutic effectiveness. The aim of the study was to assess the reliability and sensitivity of quantitative and objective assessment of upper limb performance computed by means of the robotic device and to evaluate the correlation with clinical and functional markers of the disease severity. Methods: Here we assess upper limb performances by means of the InMotion Arm Robot, a robot designed for clinical neurological applications, in a cohort of 14 children and young adults affected by FRDA, matched for age and gender with 18 healthy subjects. We focused on the analysis of kinematics, accuracy, smoothness, and submovements of the upper limb while reaching movements were performed. The robotic evaluation of upper limb performance consisted of planar reaching movements performed with the robotic system. The motors of the robot were turned off, so that the device worked as a measurement tool. The status of the disease was scored using the Scale for the Assessment and Rating of Ataxia (SARA). Relationships between robotic indices and a range of clinical and disease characteristics were examined. Results: All our robotic indices were significantly different between the two cohorts except for two, and were highly and reliably discriminative between healthy and subjects with FRDA. In particular, subjects with FRDA exhibited slower movements as well as loss of accuracy and smoothness, which are typical of the disease. Duration of Movement, Normalized Jerk, and Number of Submovements were the best discriminative indices, as they were directly and easily measurable and correlated with the status of the disease, as measured by SARA. Conclusions: Our results suggest that outcome measures obtained by means of robotic devices can improve the sensitivity of clinical evaluations of patients’ dexterity and can accurately and efficiently quantify changes over time in clinical trials, particularly when functional scales appear to be no longer sensitive

Quantitative Assessment of Motor Deficit with an Intelligent Key Object: A Pilot Study

International audienceConventional assessment of sensorimotor functions is carried out using standard clinical scales which are subjective and insufficiently sensitive to changes in motor performance. Alternatively, sensor based systems offer a quantitative approach to motor assessment. We have designed a set of low cost, easy to use instrumented objects to assess a subject's performance during skilled tasks. In this pilot study we discuss the design of one object, the intelligent key, and describe how it can be used to assess a subject's performance during fine manipulation tasks using the proposed metrics and techniques. Three subjects with motor disability and one healthy subject participated in this study. Subjects performed insertion and rotation tasks that mimic the skills used in day to day key manipulation. A threshold detector algorithm based on Teager Energy Operator was applied to the object acceleration signal to quantify time spent struggling with the task and Spectral Arc Length was used to assess the smoothness of pronation/supination. Overall, the results indicate that increased difficulty in task performance correlates with decreased smoothness in task performance

Quantitative Kinematic Characterization of Reaching Impairments in Mice After a Stroke

Background and Objective. Kinematic analysis of reaching movements is increasingly used to evaluate upper extremity function after cerebrovascular insults in humans and has also been applied to rodent models. Such analyses can require time-consuming frame-by-frame inspections and are affected by the experimenter's bias. In this study, we introduce a semi-automated algorithm for tracking forepaw movements in mice. This methodology allows us to calculate several kinematic measures for the quantitative assessment of performance in a skilled reaching task before and after a focal cortical stroke. Methods. Mice were trained to reach for food pellets with their preferred paw until asymptotic performance was achieved. Photothrombosis was then applied to induce a focal ischemic injury in the motor cortex, contralateral to the trained limb. Mice were tested again once a week for 30 days. A high frame rate camera was used to record the movements of the paw, which was painted with a nontoxic dye. An algorithm was then applied off-line to track the trajectories and to compute kinematic measures for motor performance evaluation. Results. The tracking algorithm proved to be fast, accurate, and robust. A number of kinematic measures were identified as sensitive indicators of poststroke modifications. Based on end-point measures, ischemic mice appeared to improve their motor performance after 2 weeks. However, kinematic analysis revealed the persistence of specific trajectory adjustments up to 30 days poststroke, indicating the use of compensatory strategies. Conclusions. These results support the use of kinematic analysis in mice as a tool for both detection of poststroke functional impairments and tracking of motor improvements following rehabilitation. Similar studies could be performed in parallel with human studies to exploit the translational value of this skilled reaching analysis

Goal Set Inverse Optimal Control and Iterative Re-planning for Predicting Human Reaching Motions in Shared Workspaces

To enable safe and efficient human-robot collaboration in shared workspaces it is important for the robot to predict how a human will move when performing a task. While predicting human motion for tasks not known a priori is very challenging, we argue that single-arm reaching motions for known tasks in collaborative settings (which are especially relevant for manufacturing) are indeed predictable. Two hypotheses underlie our approach for predicting such motions: First, that the trajectory the human performs is optimal with respect to an unknown cost function, and second, that human adaptation to their partner's motion can be captured well through iterative re-planning with the above cost function. The key to our approach is thus to learn a cost function which "explains" the motion of the human. To do this, we gather example trajectories from pairs of participants performing a collaborative assembly task using motion capture. We then use Inverse Optimal Control to learn a cost function from these trajectories. Finally, we predict reaching motions from the human's current configuration to a task-space goal region by iteratively re-planning a trajectory using the learned cost function. Our planning algorithm is based on the trajectory optimizer STOMP, it plans for a 23 DoF human kinematic model and accounts for the presence of a moving collaborator and obstacles in the environment. Our results suggest that in most cases, our method outperforms baseline methods when predicting motions. We also show that our method outperforms baselines for predicting human motion when a human and a robot share the workspace.Comment: 12 pages, Accepted for publication IEEE Transaction on Robotics 201

Thigh-Derived Inertial Sensor Metrics to Assess the Sit-to-Stand and Stand-to-Sit Transitions in the Timed Up and Go (TUG) Task for Quantifying Mobility Impairment in Multiple Sclerosis

INTRODUCTION: Inertial sensors generate objective and sensitive metrics of movement disability that may indicate fall risk in many clinical conditions including multiple sclerosis (MS). The Timed-Up-And-Go (TUG) task is used to assess patient mobility because it incorporates clinically-relevant submovements during standing. Most sensor-based TUG research has focused on the placement of sensors at the spine, hip or ankles; an examination of thigh activity in TUG in multiple sclerosis is wanting. METHODS: We used validated sensors (x-IMU by x-io) to derive transparent metrics for the sit-to-stand (SI-ST) transition and the stand-to-sit (ST-SI) transition of TUG, and compared effect sizes for metrics from inertial sensors on the thighs to effect sizes for metrics from a sensor placed at the L3 level of the lumbar spine. 23 healthy volunteers were compared to 17 ambulatory persons with MS (PwMS, HAI <= 2). RESULTS: During the SI-ST transition, the metric with the largest effect size comparing healthy volunteers to PwMS was the Area Under the Curve of the thigh angular velocity in the pitch direction -- representing both thigh and knee extension; the peak of the spine pitch angular velocity during SI-ST also had a large effect size, as did some temporal measures of duration of SI-ST, although less so. During the ST-SI transition the metric with the largest effect size in PwMS was the peak of the spine angular velocity curve in the roll direction. A regression was performed. DISCUSSION: We propose for PwMS that the diminished peak angular velocities during SI-ST directly represents extensor weakness, while the increased roll during ST-SI represents diminished postural control. CONCLUSIONS: During the SI-ST transition of TUG, angular velocities can discriminate between healthy volunteers and ambulatory PwMS better than temporal features. Sensor placement on the thighs provides additional discrimination compared to sensor placement at the lumbar spine