Recognition of elementary arm movements using orientation of a tri-axial accelerometer located near the wrist

In this paper we present a method for recognising three fundamental movements of the human arm (reach and retrieve, lift cup to mouth, rotation of the arm) by determining the orientation of a tri-axial accelerometer located near the wrist. Our objective is to detect the occurrence of such movements performed with the impaired arm of a stroke patient during normal daily activities as a means to assess their rehabilitation. The method relies on accurately mapping transitions of predefined, standard orientations of the accelerometer to corresponding elementary arm movements. To evaluate the technique, kinematic data was collected from four healthy subjects and four stroke patients as they performed a number of activities involved in a representative activity of daily living, 'making-a-cup-of-tea'. Our experimental results show that the proposed method can independently recognise all three of the elementary upper limb movements investigated with accuracies in the range 91–99% for healthy subjects and 70–85% for stroke patients

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

Movement fluidity of the impaired arm during stroke rehabilitation

We present an initial study on the measure of movement fluidity of the upper arm for 4 stroke patients for a duration of 3 weeks as they performed an archetypal activity of daily living – ‘making-a-cup-of-tea’ in an uncontrolled environment. Results of two complimenting measures – jerk metric and peak number computed from accelerometer data on the wrist are in agreement with the clinical scores from the Box and Block test and the Nine Hole Peg tes

Real-time arm movement recognition using FPGA

In this paper we present a FPGA-based system to detect three elementary arm movements in real-time (reach and retrieve, lift cup to mouth, rotation of the arm) using data from a wrist-worn accelerometer. Recognition is carried out by accurately mapping transitions of predefined, standard orientations of an accelerometer to the corresponding arm movements. The algorithm is coded in HDL and synthesized on the Altera DE2-115 FPGA board. For real-time operation, interfacing between the streaming sensor unit, host PC and the FPGA was achieved through a combination of Bluetooth, RS232 and an application software developed in C# using the .NET framework to facilitate serial port controls. The synthesized design used 1804 logic elements and recognised the performed arm movement in 41.2 μs, @50 MHz clock on the FPGA. Our experimental results show that the system can recognise all three arm movements with accuracies ranging 85%-96% for healthy subjects and 63%-75% for stroke survivors involved in 'making-a-cup-of-tea', typical of an activity of daily living (ADL)

Low-complexity framework for movement classification using body-worn sensors

We present a low-complexity framework for classifying elementary arm-movements (reach-retrieve, lift-cup-to-mouth, rotate-arm) using wrist-worn, inertial sensors. We propose that this methodology could be used as a clinical tool to assess rehabilitation progress in neurodegenerative pathologies tracking occurrence of specific movements performed by patients with their paretic arm. Movements performed in a controlled training-phase are processed to form unique clusters in a multi-dimensional feature-space. Subsequent movements performed in an uncontrolled testing-phase are associated to the proximal cluster using a minimum distance classifier (MDC). The framework involves performing the compute-intensive clustering on the training-dataset offline (Matlab) whereas the computation of selected features on the testing-dataset and the minimum distance (Euclidean) from pre-computed cluster centroids are done in hardware with an aim of low-power execution on sensor nodes.The architecture for feature-extraction and MDC are realized using Coordinate Rotation Digital Computer based design which classifies a movement in (9n+31) clock cycles, n being number of data samples. The design synthesized in STMicroelectronics 130nm technology consumed 5.3 nW @50 HZ, besides being functionally verified upto 20 MHz, making it applicable for real-time high-speed operations. Our experimental results show that the system can recognize all three arm-movements with average accuracies of 86% and 72% for four healthy subjects using accelerometer and gyroscope data respectively, whereas for stroke survivors the average accuracies were 67% and 60%. The framework was further demonstrated as a FPGA-based real-time system, interfacing with a streaming sensor unit