Recognition of elementary upper limb movements in an activity of daily living using data from wrist mounted accelerometers

In this paper we present a methodology as a proof of concept for recognizing fundamental movements of the humanarm (extension, flexion and rotation of the forearm) involved in ‘making-a-cup-of-tea’, typical of an activity of daily-living (ADL). The movements are initially performed in a controlled environment as part of a training phase and the data are grouped into three clusters using k-means clustering. Movements performed during ADL, forming part of the testing phase, are associated with each cluster label using a minimum distance classifier in a multi-dimensional feature space, comprising of features selected from a ranked set of 30 features, using Euclidean and Mahalonobis distance as the metric. Experiments were performed with four healthy subjects and our results show that the proposed methodology can detect the three movements with an overall average accuracy of 88% across all subjects and arm movement types using Euclidean distance classifier

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

An Investigation into the Accuracy of Calculating upper Body Joint Angles Using MARG Sensors

We investigate magnetic, angular rate, and gravity (MARG) sensor modules for deriving shoulder, elbow and lumbar joint angles of the human body. We use three tri-axial MARG sensors, placed proximal to the wrist and elbow and centrally on the chest, and employ a quaternion-based Unscented Kalman Filter technique to estimate orientations from the sensor data, from which joint angles are calculated based on a simple model of the arm. Tests reveal that the method has the potential to accurately derive specific angles. When compared with a camera-based system, a root mean square difference error between 5° - 15° was observed

Machine Learning for Run-Time Energy Optimisation in Many-Core Systems

In recent years, the focus of computing has moved away from performance-centric serial computation to energy-efficient parallel computation. This necessitates run-time optimisation techniques to address the dynamic resource requirements of different applications on many-core architectures. In this paper, we report on intelligent run-time algorithms which have been experimentally validated for managing energy and application performance in many-core embedded system. The algorithms are underpinned by a cross-layer system approach where the hardware, system software and application layers work together to optimise the energy-performance trade-off. Algorithm development is motivated by the biological process of how a human brain (acting as an agent) interacts with the external environment (system) changing their respective states over time. This leads to a pay-off for the action taken, and the agent eventually learns to take the optimal/best decisions in future. In particular, our online approach uses a model-free reinforcement learning algorithm that suitably selects the appropriate voltage-frequency scaling based on workload prediction to meet the applications’ performance requirements and achieve energy savings of up to 16% in comparison to state-of-the-art-techniques, when tested on four ARM A15 cores of an ODROID-XU3 platform

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

Recognition of upper limb movements for remote health monitoring

In this paper we present two methodologies based on a systematic exploration to recognize three fundamental movements of the human forearm (extension, flexion and rotation) performed during an archetypal activity of daily-living (ADL) - ‘making-a-cup-of-tea’ by four healthy subjects and stroke survivors. The recognition methodologies have been further implemented in hardware (ASIC/FPGA) which can be embedded on a resource constrained WSN node for real-time detection of arm movements. We propose that these techniques 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 the paretic arm throughout the da

A CORDIC-based low-power statistical feature computation engine for WSN applications

In this paper we present a carry-save arithmetic (CSA) based Coordinate Rotation Digital Computer (CORDIC) engine for computing eight fundamental time domain statistical features. These features are used commonly in association with major classifiers in remote health monitoring systems with an aim of executing them on a node of Wireless Sensor Network (WSN). The engine computes all the eight features sequentially in 3n clock cycles where n is the number of data samples. We further present a comparative analysis of the hardware complexity of our proposed architecture with an alternate architecture which does not use CORDIC (instead uses standalone array multiplier, divider, square rooter and logarithm converter). The hardware complexity of the two architectures presented in terms of full adder count reflects the effectiveness of using CORDIC for the given application. The engine was synthesized using the STMicroelectronics 130 nm technology library and occupied 205K NAND2 equivalent cell area and consumed 1nW dynamic power @ 50 Hz as estimated using Prime time. Therefore, the design can be applicable for low-power real-time operations within a WSN node