Activity Inference for Ambient Intelligence Through Handling Artifacts in a Healthcare Environment

Martínez-Pérez, Francisco E.; González-Fraga, José A.; Cuevas-Tello, Juan C.; Rodríguez, Marcela D.

doi:10.3390/s120101072

Cited by 34 publications

(27 citation statements)

References 30 publications

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“…Tracking of human body movement has been performed across various applications such as home-based remote health monitoring [1][2], human computer interaction [3][4] and sports coaching [5][6] using a wide range of sensing technologies, including: mechanical tracking, optical systems, radio-frequency identification (RFID) [7], low-cost bodyworn inertial sensors [8][9], and fusion of vision-based and inertial sensor based approaches [10]. Most of these approaches are however, primarily restricted to indoor activities within a defined region and require an un-hindered surveillance of the vision system [11].…”

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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“…CORDIC is an iterative algorithm which uses 2D vector rotation for computing different transcendental functions employing the iterative equations: (1) where, [xj, yj] T , zj and σj ϵ {1, -1} are the intermediate result vector, the residual angle and the direction of vector rotation at the j-th iteration stage respectively; µ ϵ {1, 0} being the coordinate of rotation -circular and linear respectively. In each coordinate system, CORDIC in general, can be operated in two modes -Vectoring and Rotation [18].…”

Section: Algorithm To Architecture Mappingmentioning

confidence: 99%

“…Remote monitoring for long durations has been aided by the advancements ubiquitous and mobile computing facilities primarily using radio-frequency identification (RFID) [1], lowcost inertial sensors [2], and fusion of inertial sensor and visionbased approaches [3]. RFID and vision-based methods are primarily restricted to a defined region catering for indoor activities, requiring an un-hindered surveillance [4].…”

Section: Introductionmentioning

confidence: 99%

Low-Complexity Framework for Movement Classification Using Body-Worn Sensors

Biswas

Maharatna

Panić

et al. 2017

IEEE Trans. VLSI Syst.

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Abstract-We present a low-complexity framework for classifying elementary arm-movements (reach-retrieve, lift-cup-tomouth, 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 realtime 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.

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“…It is also motivated by a wide range of mobile and ubiquitous computing applications which include personalisation of user interfaces [10], further aided by the development of inertial sensors. Radio-frequency identification (RFID) has also been used to monitor the movement of objects within the home environment that are typically encountered during daily living [24]. Another approach being used is fusing data from vision systems and inertial sensors to complement each other.…”

Section: Introductionmentioning

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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Activity Inference for Ambient Intelligence Through Handling Artifacts in a Healthcare Environment

Cited by 34 publications

References 30 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

Low-Complexity Framework for Movement Classification Using Body-Worn Sensors

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

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