Recognizing upper limb movements with wrist worn inertial sensors using k-means clustering classification

In this paper we present a methodology for recognizing three fundamental movements of the human forearm (extension, flexion and rotation) using pattern recognition applied to the data from a single wrist-worn, inertial sensor. We propose that this technique could be used as a clinical tool to assess rehabilitation progress in neurodegenerative pathologies such as stroke or cerebral palsy by tracking the number of times a patient performs specific arm movements (e.g. prescribed exercises) with their paretic arm throughout the day. We demonstrate this with healthy subjects and stroke patients in a simple proof of concept study in whichthese arm movements are detected during an archetypal activity of daily-living (ADL) – ‘making-a-cup-of-tea’. Data is collected from a tri-axial accelerometer and a tri-axial gyroscope located proximal to the wrist. In a training phase, movements are initially performed in a controlled environment which are represented by a ranked set of 30 time-domain features. Using a sequential forward selection technique, for each set of feature combinations three clusters are formed using k-means clustering followed by 10 runs of 10-fold cross validation on the training data to determine the best feature combinations. For the testing phase, movements performed during the ADL are associated with each cluster label using a minimum distance classifier in a multi-dimensional feature space, comprised of the best ranked features, using Euclidean or Mahalanobis distance as the metric. Experiments were performed with four healthy subjects and four stroke survivors and our results showthat the proposed methodology can detect the three movements performed during the ADL with an overall average accuracy of 88% using the accelerometer data and 83% using the gyroscope data across all healthy subjects and arm movement types. The average accuracy across all stroke survivors was 70% using accelerometer data and 66% using gyroscope data. We also use a Linear Discriminant Analysis (LDA) classifier and a Support Vector Machine (SVM) classifier in association with the same set of features to detect the three arm movements and compare the results to demonstrate the effectiveness of our proposed methodology

CORDIC Framework for Quaternion-based Joint Angle Computation to Classify Arm Movements

We present a novel architecture for arm movement classification based on kinematic properties (joint angle and position), computed from MARG sensors, using a quaternion-based gradient-descent method and a 2-link model of the upper limb. The design based on Coordinate Rotation Digital Computer framework was validated on stroke survivors and healthy subjects performing three elementary arm movements (reach and retrieve, lift arm, rotate arm), involved in `making-a-cup-of-tea' an archetypal daily activity, achieved an overall accuracy of 78% and 85% respectively. The design coded in System Verilog, was synthesized using STMicroelectronics 130 nm technology, occupies 340K NAND2 equivalent area and consumes 292 nW @ 150 Hz, besides being functionally verified up to 25 MHz making it suitable for real-time high speed operations. The orientation, arm position and the joint angle, are computed on-the-fly, with the classification performed at the end of movement duration

CORDIC Framework for Quaternion-based Joint Angle Computation to Classify Arm Movements

We present a novel architecture for arm movement classification based on kinematic properties (joint angle and position), computed from MARG sensors, using a quaternion-based gradient-descent method and a 2-link model of the upper limb. The design based on Coordinate Rotation Digital Computer framework was validated on stroke survivors and healthy subjects performing three elementary arm movements (reach and retrieve, lift arm, rotate arm), involved in `making-a-cup-of-tea' an archetypal daily activity, achieved an overall accuracy of 78% and 85% respectively. The design coded in System Verilog, was synthesized using STMicroelectronics 130 nm technology, occupies 340K NAND2 equivalent area and consumes 292 nW @ 150 Hz, besides being functionally verified up to 25 MHz making it suitable for real-time high speed operations. The orientation, arm position and the joint angle, are computed on-the-fly, with the classification performed at the end of movement duration

Novel Use of a Synthetic Training Device in the Rehabilitation of Chronic Neck Pain of Rotary Rear Crew

BACKGROUND: Chronic flight-related neck pain is a common, well-recognized problem in military aircrew. The reasons for flight-related neck pain are multifactorial; however, there are currently no evidence-based guidelines for its prevention or clinical management. This case study describes the novel use of a synthetic training device in the rehabilitation of a Chinook crewman with chronic neck pain.CASE REPORT: The patient is a 34-yr-old rear crewman with 10 yr flying experience in the Chinook helicopter. He has a history of intermittent neck and shoulder pain since 2009 following a rugby injury. Over the years he has self-managed recurrent episodes of neck pain. However, in November 2017 his pain was so severe that he could no longer continue flying. This pain made him unfit for flying duties for 18 mo and he received intensive rehabilitation and injection therapy. RAF Odiham’s new flying simulator was used in his return to flying program, so enabling him to become fully fit and return to all flying duties.DISCUSSION: Management and treatment of chronic flight-related neck pain is challenging. This case study highlights the importance of a multifactorial management approach and how a synthetic training device can be used in the rehabilitation of rotary rear crew.Jobges S, Leaming F. Novel use of a synthetic training device in the rehabilitation of chronic neck pain of rotary rear crew. Aerosp Med Hum Perform. 2020; 91(4):369–372.</jats:p

Rehab-Net: Deep Learning framework for Arm Movement Classification using Wearable Sensors for Stroke Rehabilitation

In this paper, we present a deep learning framework 'Rehab-Net' for effectively classifying three upper limb movements of the human arm, involving extension, flexion and rotation of the forearm which over the time could provide a measure of rehabilitation progress. The proposed framework, Rehab-Net is formulated with a personalized, light weight and low complex, customized CNN model, using 2-layers of Convolutional neural network (CNN), interleaved with pooling layers, followed by a fully-connected layer that classifies the three movements from tri-axial acceleration input data collected from the wrist. The proposed Rehab-net framework was validated on sensor data collected in two situations-a) seminaturalistic environment involving an archetypal activity of 'making-tea' with 4 stroke survivors and b) natural environment, where 10 stroke survivors were free to perform any desired arm movement for a duration of 120 minutes. We achieve an overall accuracy of 97.89% on semi-naturalistic data and 88.87% on naturalistic data which exceeded state-of-the-art learning algorithms namely, Linear Discriminant Analysis, Support Vector Machines, and k-means clustering with an average accuracy of 48.89%, 44.14% and 27.64%. Subsequently, a computational complexity analysis of the proposed model has been discussed with an eye towards hardware implementation. The clinical significance of this study is to accurately monitor the clinical progress of the rehabilitated subjects under the ambulatory settings

Prevalence of mild cognitive impairment in employable patients after acute coronary event in cardiac rehabilitation

INTRODUCTION: Adequate cognitive function in patients is a prerequisite for successful implementation of patient education and lifestyle coping in comprehensive cardiac rehabilitation (CR) programs. Although the association between cardiovascular diseases and cognitive impairments (CIs) is well known, the prevalence particularly of mild CI in CR and the characteristics of affected patients have been insufficiently investigated so far. METHODS: In this prospective observational study, 496 patients (54.5 ± 6.2 years, 79.8% men) with coronary artery disease following an acute coronary event (ACE) were analyzed. Patients were enrolled within 14 days of discharge from the hospital in a 3-week inpatient CR program. Patients were tested for CI using the Montreal Cognitive Assessment (MoCA) upon admission to and discharge from CR. Additionally, sociodemographic, clinical, and physiological variables were documented. The data were analyzed descriptively and in a multivariate stepwise backward elimination regression model with respect to CI. RESULTS: At admission to CR, the CI (MoCA score < 26) was determined in 182 patients (36.7%). Significant differences between CI and no CI groups were identified, and CI group was associated with high prevalence of smoking (65.9 vs 56.7%, P = 0.046), heavy (physically demanding) workloads (26.4 vs 17.8%, P < 0.001), sick leave longer than 1 month prior to CR (28.6 vs 18.5%, P = 0.026), reduced exercise capacity (102.5 vs 118.8 W, P = 0.006), and a shorter 6-min walking distance (401.7 vs 421.3 m, P = 0.021) compared to no CI group. The age- and education-adjusted model showed positive associations with CI only for sick leave more than 1 month prior to ACE (odds ratio [OR] 1.673, 95% confidence interval 1.07–2.79; P = 0.03) and heavy workloads (OR 2.18, 95% confidence interval 1.42–3.36; P < 0.01). CONCLUSION: The prevalence of CI in CR was considerably high, affecting more than one-third of cardiac patients. Besides age and education level, CI was associated with heavy workloads and a longer sick leave before ACE

The Effects of Exercise on Balance in Persons with Parkinson's Disease: A Systematic Review Across the Disability Spectrum

Background and Purpose: Parkinson's disease is a progressive neurodegenerative disorder that affects neurophysiologic function, movement abilities, and quality of life (QOL). Research examining the effects of exercise has suggested benefits related to a variety of outcomes; however, no reviews have synthesized research findings across the spectrum of disability. This project sought to systematically review studies that examined the impact of exercise interventions on balance outcomes for people with Parkinson's disease, within the categories defined by the World Health Organization in the International Classification of Functioning, Disability, and Health (ICF) model. Methods: A systematic review of medical literature databases was performed using keywords Parkinson's disease and exercise. Studies were eligible if the intervention included exercise and examined variables within one of the three ICF categories. Following the ICF model, outcomes regarding Body Structure and Function, Activity, and Participation were measured, respectively, in terms of postural instability, balance task performance, and QOL and fall events. Results: Within the Body Structure and Function category, there was moderate evidence that exercise resulted in improvements in postural instability. Within the Activity category, there was moderate evidence that exercise was effective for improving balance task performance. In contrast, within the Participation category, there was limited evidence that exercise resulted in improvements in QOL measures or fall events. Discussion and Conclusions: Regardless of the strength of the evidence, the studies reviewed all report that exercise resulted in improvements in postural stability and balance task performance. Despite these improvements, the number and quality of the studies and the outcomes used were limited. There is a need for longer term follow-up to establish trajectory of change and to determine if any gains are retained long term. The optimal delivery and content of exercise interventions (dosing, component exercises) at different stages of the disease are not clear

Comparing exercise in Parkinson's disease-the Berlin BIG Study

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