Online motion recognition using an accelerometer in a mobile device

Fuentes, Daniel; Abril, Luis González; Ángulo, Cecilio; Ramírez, Juan Antonio Ortega

doi:10.1016/j.eswa.2011.08.098

Cited by 61 publications

(32 citation statements)

References 10 publications

(2 reference statements)

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“…Different machine learning techniques have been used for such movement recognition, e.g. Support Vector Machines (SVM) [16][17], Decision Trees (DT) [8,15], Naive Bayes (NB) [15], Multi-Layer Perceptron (MLP) [18], Artificial Neural Networks (ANN) [8], Hidden Markov Models (HMM) [19], or a combination of these techniques [10]. Instancebased classification algorithms have also been used successfully to classify data from inertial sensors but suffer from high memory usage and long processing times [20].…”

Section: Introductionmentioning

confidence: 99%

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

Biswas

Cranny

Gupta

et al. 2015

Human Movement Science

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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 which these 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 Mahalonobis distance as the metric. Experiments were performed with four healthy subjects and four stroke survivors and our results show that 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.

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Section: Introductionmentioning

confidence: 99%

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

Biswas

Cranny

Gupta

et al. 2015

Human Movement Science

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“…In these cases, computational cost is no limitation and hence methods of a more complex nature can be used. In contrast, efficiency is a crucial issue when processing is carried out within the mobile device itself (Fuentes, Gonzalez-Abril, Angulo, & Ortega, 2012;Reddy et al, 2010).…”

Section: Introductionmentioning

confidence: 99%

Discrete techniques applied to low-energy mobile human activity recognition. A new approach

Concepción

Morillo

Abril

et al. 2014

Expert Systems with Applications

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Human activity recognition systems are currently implemented by hundreds of applications and, in recent years, several technology manufacturers have introduced new wearable devices for this purpose. Battery consumption constitutes a critical point in these systems since most are provided with a rechargeable battery. In this paper, by using discrete techniques based on the Ameva algorithm, an innovative approach for human activity recognition systems on mobile devices is presented. Furthermore, unlike other systems in current use, this proposal enables recognition of high granularity activities by using accelerometer sensors. Hence, the accuracy of activity recognition systems can be increased without sacrificing efficiency. A comparative is carried out between the proposed approach and an approach based on the well-known neural networks.

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“…However, the accuracy of any movement classification technique is dependent on the system components and requirements, covering areas such as: type of activities, number of activities, type of sensors, number of sensors, placement of sensors [23], level of data fusion, etc.…”

Section: Introductionmentioning

confidence: 99%

“…This is particularly relevant when considering the increasing healthcare costs of an ageing population, especially those related to the treatment of chronic arthritis, cardiovascular diseases or neurodegenerative diseases, and the desire to reduce the amount of time the patient spends at the clinic. Research and development in to Wireless Body Area Networks (WBANs), wherein a patient wears a number of sensors either directly on their body or contained within clothing or other facilitators [23], have enabled user data to be captured in a ubiquitous and continuous manner within natural environments. In such wearable systems, the data analysis primarily needs to be carried out at the sensor node yielding energy efficient solutions as compared to conventional remote monitoring approaches based on continuous transmission of vital sign data [3].…”

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confidence: 99%

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

et al. 2014

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Abstract: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.

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Online motion recognition using an accelerometer in a mobile device

Cited by 61 publications

References 10 publications

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

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

Discrete techniques applied to low-energy mobile human activity recognition. A new approach

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

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