The Pragmatic Classification of Upper Extremity Motion in Neurological Patients: A Primer

Wearable-Based Kinematic Analysis of Upper-Limb Movements During Daily Activities Could Provide Insights into Stroke Survivors' Motor Ability

BACKGROUND

Frequent and objective monitoring of motor recovery progression holds significant importance in stroke rehabilitation. Despite extensive studies on wearable solutions in this context, the focus has been predominantly on evaluating limb activity. This study aims to address this limitation by delving into a novel measure of wrist kinematics more intricately related to patients' motor capacity.

OBJECTIVE

To explore a new wearable-based approach for objectively and reliably assessing upper-limb motor ability in stroke survivors using a single inertial sensor placed on the stroke-affected wrist.

METHODS

Seventeen stroke survivors performed a series of daily activities within a simulated home setting while wearing a six-axis inertial measurement unit on the wrist affected by stroke. Inertial data during point-to-point upper-limb movements were decomposed into movement segments, from which various kinematic variables were derived. A data-driven approach was then employed to identify a kinematic variable demonstrating robust internal reliability, construct validity, and convergent validity.

RESULTS

We have identified a key kinematic variable, namely the 90th percentile of movement segment distance during point-to-point movements. This variable exhibited robust reliability (intra-class correlation coefficient of .93) and strong correlations with established clinical measures of motor capacity (Pearson's correlation coefficients of .81 with the Fugl-Meyer Assessment for Upper-Extremity; .77 with the Functional Ability component of the Wolf Motor Function Test; and -.68 with the Performance Time component of the Wolf Motor Function Test).

CONCLUSIONS

The findings underscore the potential for continuous, objective, and convenient monitoring of stroke survivors' motor progression throughout rehabilitation.

The MIGREX study: Prevalence and risk factors of sexual dysfunction among migraine patients

BACKGROUND

Migraine attacks have a high impact on daily activities. There is limited research on the burden of migraine on sexual functioning.

OBJECTIVE

To determine the prevalence of sexual dysfunction in patients with migraine and its relationship with migraine features and comorbidities.

METHOD

This is a cross-sectional study. We included migraine patients between 18 and 60 years-old from 8 Headache Clinics in Spain. We recorded demographic data and migraine features. Patients fulfilled a survey including comorbidities, Arizona Sexual Experiences Scale, Hospital Anxiety and Depression Scale and a questionnaire about migraine impact on sexual activity. A K-nearest neighbor supervised learning algorithm was used to identify differences between migraine patients with and without sexual dysfunction.

RESULTS

We included 306 patients (85.6% women, mean age 42.3±11.1 years). A 41.8% of participants had sexual dysfunction. Sexual dysfunction was associated with being female (OR [95% CI]: 2.42 [1.17-5.00]; p<0.001), being older than 46.5 years (4.04 [2.48-6.59]; p<0.001), having chronic migraine (2.31 [1.41-3.77]; p=0.001), using preventive medication (2.45 [1.35-4.45]; p=0.004), analgesic overusing (3.51 [2.03-6.07]; p<0.001), menopause (4.18 [2.43-7.17]; p<0.001) and anxiety (2.90 [1.80-4.67]; p<0.001) and depression (6.14 [3.18-11.83]; p<0.001). However, only female gender, age, menopause and depression were the statistically significant variables selected in the model to classify migraine patients with or without sexual dysfunction (Accuracy [95% CI]: 0.75 (0.62-0.85), Kappa: 0.48, p=0.005).

CONCLUSIONS

Sexual dysfunction is frequent in migraine patients visited in a headache clinic. However, migraine characteristics or use of preventive medication are not directly associated with sexual dysfunction. Instead, risk factors for sexual dysfunction were female gender, higher age, menopause and depression.

The use of wearable sensors to assess and treat the upper extremity after stroke: a scoping review

PURPOSE

To address the gap in the literature and clarify the expanding role of wearable sensor data in stroke rehabilitation, we summarized the methods for upper extremity (UE) sensor-based assessment and sensor-based treatment.

MATERIALS AND METHODS

The guideline outlined by the preferred reporting items for systematic reviews and meta-analysis extension for scoping reviews was used to complete this scoping review. Information pertaining to participant demographics, sensory information, data collection, data processing, data analysis, and study results were extracted from the studies for analysis and synthesis.

RESULTS

We included 43 articles in the final review. We organized the results into assessment and treatment categories. The included articles used wearable sensors to identify UE functional motion, categorize motor impairment/activity limitation, and quantify real-world use. Wearable sensors were also used to augment UE training by triggering sensory cues or providing instructional feedback about the affected UE.

CONCLUSIONS

Sensors have the potential to greatly expand assessment and treatment beyond traditional clinic-based approaches. This capability could support the quantification of rehabilitation dose, the nuanced assessment of impairment and activity limitation, the characterization of daily UE use patterns in real-world settings, and augment UE training adherence for home-based rehabilitation.IMPLICATIONS FOR REHABILITATIONSensor data have been used to assess UE functional motion, motor impairment/activity limitation, and real-world use.Sensor-assisted treatment approaches are emerging, and may be a promising tool to augment UE adherence in home-based rehabilitation.Wearable sensors may extend our ability to objectively assess UE motion beyond supervised clinical settings, and into home and community settings.

IMU-Based Monitoring for Assistive Diagnosis and Management of IoHT: A Review

With the rapid development of Internet of Things (IoT) technologies, traditional disease diagnoses carried out in medical institutions can now be performed remotely at home or even ambient environments, yielding the concept of the Internet of Health Things (IoHT). Among the diverse IoHT applications, inertial measurement unit (IMU)-based systems play a significant role in the detection of diseases in many fields, such as neurological, musculoskeletal, and mental. However, traditional numerical interpretation methods have proven to be challenging to provide satisfying detection accuracies owing to the low quality of raw data, especially under strong electromagnetic interference (EMI). To address this issue, in recent years, machine learning (ML)-based techniques have been proposed to smartly map IMU-captured data on disease detection and progress. After a decade of development, the combination of IMUs and ML algorithms for assistive disease diagnosis has become a hot topic, with an increasing number of studies reported yearly. A systematic search was conducted in four databases covering the aforementioned topic for articles published in the past six years. Eighty-one articles were included and discussed concerning two aspects: different ML techniques and application scenarios. This review yielded the conclusion that, with the help of ML technology, IMUs can serve as a crucial element in disease diagnosis, severity assessment, characteristic estimation, and monitoring during the rehabilitation process. Furthermore, it summarizes the state-of-the-art, analyzes challenges, and provides foreseeable future trends for developing IMU-ML systems for IoHT.

PrimSeq: A deep learning-based pipeline to quantitate rehabilitation training

Stroke rehabilitation seeks to accelerate motor recovery by training functional activities, but may have minimal impact because of insufficient training doses. In animals, training hundreds of functional motions in the first weeks after stroke can substantially boost upper extremity recovery. The optimal quantity of functional motions to boost recovery in humans is currently unknown, however, because no practical tools exist to measure them during rehabilitation training. Here, we present PrimSeq, a pipeline to classify and count functional motions trained in stroke rehabilitation. Our approach integrates wearable sensors to capture upper-body motion, a deep learning model to predict motion sequences, and an algorithm to tally motions. The trained model accurately decomposes rehabilitation activities into elemental functional motions, outperforming competitive machine learning methods. PrimSeq furthermore quantifies these motions at a fraction of the time and labor costs of human experts. We demonstrate the capabilities of PrimSeq in previously unseen stroke patients with a range of upper extremity motor impairment. We expect that our methodological advances will support the rigorous measurement required for quantitative dosing trials in stroke rehabilitation.

Stroke commonly damages motor function in the upper extremity (UE), leading to long-term disability and loss of independence in a majority of individuals. Rehabilitation seeks to restore function by training daily activities, which deliver repeated UE functional motions. The optimal number of functional motions necessary to boost recovery is unknown. This gap stems from the lack of measurement tools to feasibly count functional motions. We thus developed the PrimSeq pipeline to enable the accurate and rapid counting of building-block functional motions, called primitives. PrimSeq uses wearable sensors to capture rich motion information from the upper body, and custom-built algorithms to detect and count functional primitives in this motion data. We showed that our deep learning algorithm precisely counts functional primitives performed by stroke patients and outperformed other benchmark algorithms. We also showed patients tolerated the wearable sensors and that the approach is 366 times faster at counting primitives than humans. PrimSeq thus provides a precise and practical means of quantifying functional primitives, which promises to advance stroke research and clinical care and to improve the outcomes of individuals with stroke.

The MIGREX study: Prevalence and risk factors of sexual dysfunction among migraine patients

BACKGROUND

Migraine attacks have a high impact on daily activities. There is limited research on the burden of migraine on sexual functioning.

OBJECTIVE

To determine the prevalence of sexual dysfunction in patients with migraine and its relationship with migraine features and comorbidities.

METHOD

This is a cross-sectional study. We included migraine patients between 18 and 60 years-old from 8 Headache Clinics in Spain. We recorded demographic data and migraine features. Patients fulfilled a survey including comorbidities, Arizona Sexual Experiences Scale, Hospital Anxiety and Depression Scale and a questionnaire about migraine impact on sexual activity. A K-nearest neighbor supervised learning algorithm was used to identify differences between migraine patients with and without sexual dysfunction.

RESULTS

We included 306 patients (85.6% women, mean age 42.3±11.1 years). A 41.8% of participants had sexual dysfunction. Sexual dysfunction was associated with being female (OR [95% CI]: 2.42 [1.17-5.00]; p<0.001), being older than 46.5 years (4.04 [2.48-6.59]; p<0.001), having chronic migraine (2.31 [1.41-3.77]; p=0.001), using preventive medication (2.45 [1.35-4.45]; p=0.004), analgesic overusing (3.51 [2.03-6.07]; p<0.001), menopause (4.18 [2.43-7.17]; p<0.001) and anxiety (2.90 [1.80-4.67]; p<0.001) and depression (6.14 [3.18-11.83]; p<0.001). However, only female gender, age, menopause and depression were the statistically significant variables selected in the model to classify migraine patients with or without sexual dysfunction (Accuracy [95% CI]: 0.75 (0.62-0.85), Kappa: 0.48, p=0.005).

CONCLUSIONS

Sexual dysfunction is frequent in migraine patients visited in a headache clinic. However, migraine characteristics or use of preventive medication are not directly associated with sexual dysfunction. Instead, risk factors for sexual dysfunction were female gender, higher age, menopause and depression.

Towards data-driven stroke rehabilitation via wearable sensors and deep learning

Recovery after stroke is often incomplete, but rehabilitation training may potentiate recovery by engaging endogenous neuroplasticity. In preclinical models of stroke, high doses of rehabilitation training are required to restore functional movement to the affected limbs of animals. In humans, however, the necessary dose of training to potentiate recovery is not known. This ignorance stems from the lack of objective, pragmatic approaches for measuring training doses in rehabilitation activities. Here, to develop a measurement approach, we took the critical first step of automatically identifying functional primitives, the basic building block of activities. Forty-eight individuals with chronic stroke performed a variety of rehabilitation activities while wearing inertial measurement units (IMUs) to capture upper body motion. Primitives were identified by human labelers, who labeled and segmented the associated IMU data. We performed automatic classification of these primitives using machine learning. We designed a convolutional neural network model that outperformed existing methods. The model includes an initial module to compute separate embeddings of different physical quantities in the sensor data. In addition, it replaces batch normalization (which performs normalization based on statistics computed from the training data) with instance normalization (which uses statistics computed from the test data). This increases robustness to possible distributional shifts when applying the method to new patients. With this approach, we attained an average classification accuracy of 70%. Thus, using a combination of IMU-based motion capture and deep learning, we were able to identify primitives automatically. This approach builds towards objectively-measured rehabilitation training, enabling the identification and counting of functional primitives that accrues to a training dose.

Evaluation of Pattern Recognition Methods for Head Gesture-Based Interface of a Virtual Reality Helmet Equipped with a Single IMU Sensor

The motivation of this paper is to examine the effectiveness of state-of-the-art and newly proposed motion capture pattern recognition methods in the task of head gesture classifications. The head gestures are designed for a user interface that utilizes a virtual reality helmet equipped with an internal measurement unit (IMU) sensor that has 6-axis accelerometer and gyroscope. We will validate a classifier that uses Principal Components Analysis (PCA)-based features with various numbers of dimensions, a two-stage PCA-based method, a feedforward artificial neural network, and random forest. Moreover, we will also propose a Dynamic Time Warping (DTW) classifier trained with extension of DTW Barycenter Averaging (DBA) algorithm that utilizes quaternion averaging and a bagged variation of previous method (DTWb) that utilizes many DTW classifiers that perform voting. The evaluation has been performed on 975 head gesture recordings in seven classes acquired from 12 persons. The highest value of recognition rate in a leave-one-out test has been obtained for DTWb and it equals 0.975 (0.026 better than the best of state-of-the-art methods to which we have compared our approach). Among the most important applications of the proposed method is improving life quality for people who are disabled below the neck by supporting, for example, an assistive autonomous power chair with a head gesture interface or remote controlled interfaces in robotics.