Quantitative measures of smoothness play an important role in the assessment of sensorimotor impairment and motor learning. Traditionally, movement smoothness has been computed mainly for discrete movements, in particular arm, reaching and circle drawing, using kinematic data. There are currently very few studies investigating smoothness of rhythmic movements, and there is no systematic way of analysing the smoothness of such movements. There is also very little work on the smoothness of other movement related variables such as force, impedance etc. In this context, this paper presents the first step towards a unified framework for the analysis of smoothness of arbitrary movements and using various data. It starts with a systematic definition of movement smoothness and the different factors that influence smoothness, followed by a review of existing methods for quantifying the smoothness of discrete movements. A method is then introduced to analyse the smoothness of rhythmic movements by generalising the techniques developed for discrete movements. We finally propose recommendations for analysing smoothness of any general sensorimotor behaviour.Electronic supplementary materialThe online version of this article (doi:10.1186/s12984-015-0090-9) contains supplementary material, which is available to authorized users.

The need for movement smoothness quantification to assess motor learning and recovery has resulted in various measures that look at different aspects of a movement's profile. This paper first shows that most of the previously published smoothness measures lack validity, consistency, sensitivity, or robustness. It then introduces and evaluates the spectral arc-length metric that uses a movement speed profile's Fourier magnitude spectrum to quantify movement smoothness. This new metric is systematically tested and compared to other smoothness metrics, using experimental data from stroke and healthy subjects as well as simulated movement data. The results indicate that the spectral arc-length metric is a valid and consistent measure of movement smoothness, which is both sensitive to modifications in motor behavior and robust to measurement noise. We hope that the systematic analysis of this paper is a step toward the standardization of the quantitative assessment of movement smoothness.

The current evidence in support of the robot-assisted hand rehabilitation is preliminary but very promising, and provides a strong rationale for more systematic investigations in the future.

The structural design, control system, and integrated biofeedback for a wearable exoskeletal robot for upper extremity stroke rehabilitation are presented. Assisted with clinical evaluation, designers, engineers, and scientists have built a device for robotic assisted upper extremity repetitive therapy (RUPERT). Intense, repetitive physical rehabilitation has been shown to be beneficial overcoming upper extremity deficits, but the therapy is labor intensive and expensive and difficult to evaluate quantitatively and objectively. The RUPERT is developed to provide a low cost, safe and easy-to-use, robotic-device to assist the patient and therapist to achieve more systematic therapy at home or in the clinic. The RUPERT has four actuated degrees-of-freedom driven by compliant and safe pneumatic muscles (PMs) on the shoulder, elbow, and wrist. They are programmed to actuate the device to extend the arm and move the arm in 3-D space. It is very important to note that gravity is not compensated and the daily tasks are practiced in a natural setting. Because the device is wearable and lightweight to increase portability, it can be worn standing or sitting providing therapy tasks that better mimic activities of daily living. The sensors feed back position and force information for quantitative evaluation of task performance. The device can also provide real-time, objective assessment of functional improvement. We have tested the device on stroke survivors performing two critical activities of daily living (ADL): reaching out and self feeding. The future improvement of the device involves increased degrees-of-freedom and interactive control to adapt to a user's physical conditions.

Traditional assessment of a stroke subject's motor ability, carried out by a therapist who observes and rates the subject's motor behavior using ordinal measurements scales, is subjective, time consuming and lacks sensitivity. Rehabilitation robots, which have been the subject of intense inquiry over the last decade, are equipped with sensors that are used to develop objective measures of motor behaviors in a semiautomated way during therapy. This article reviews the current contributions of robot-assisted motor assessment of the upper limb. It summarizes the various measures related to movement performance, the models of motor recovery in stroke subjects and the relationship of robotic measures to standard clinical measures. It analyses the possibilities offered by current robotic assessment techniques and the aspects to address to make robotic assessment a mainstream motor assessment method.

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Inertial measurement units (IMUs) are increasingly used to estimate movement quality and quantity to the infer the nature of motor behavior. The current literature contains several attempts to estimate movement smoothness using data from IMUs, many of which assume that the translational and rotational kinematics measured by IMUs can be directly used with the smoothness measures spectral arc length (SPARC) and log dimensionless jerk (LDLJ-V). However, there has been no investigation of the validity of these approaches. In this paper, we systematically evaluate the use of these measures on the kinematics measured by IMUs. We show that: (a) SPARC and LDLJ-V are valid measures of smoothness only when used with velocity; (b) SPARC and LDLJ-V applied on translational velocity reconstructed from IMU is highly error prone due to drift caused by integration of reconstruction errors; (c) SPARC can be applied directly on rotational velocities measured by a gyroscope, but LDLJ-V can be error prone. For discrete translational movements, we propose a modified version of the LDLJ-V measure, which can be applied to acceleration data (LDLJ-A). We evaluate the performance of these measures using simulated and experimental data. We demonstrate that the accuracy of LDLJ-A depends on the time profile of IMU orientation reconstruction error. Finally, we provide recommendations for how to appropriately apply these measures in practice under different scenarios, and highlight various factors to be aware of when performing smoothness analysis using IMU data.

This study shows that a large proportion of severely affected stroke patients have residual EMG, which yields a direct and practical way to trigger robot-assisted training.