Daily living activity recognition based on statistical feature quality group selection

Baños, Oresti; Damas, Miguel; Pomares, H.; Prieto, Alberto; Rojas, Ignacio

doi:10.1016/j.eswa.2012.01.164

Cited by 115 publications

(88 citation statements)

References 35 publications

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“…[25]. Sensor data is collected at a rate of 50 Hz, deemed sufficient for assessing habitual limb movement which is on the higher side compared to assessing holistic activity as in [10,11]. The accelerometer and gyroscope ranges are selected at ± 1.5g and ± 500 o /sec respectively.…”

Section: Sensor Selection and Placementmentioning

confidence: 99%

“…Human activity recognition in natural settings is an active research area that has been applied widely in the field of chronic disease management and rehabilitation [10][11][12][13][14]. Various types of wearable sensors have been used for activity recognition such as accelerometers, gyroscopes and magnetometers [5,[13][14].…”

Section: Introductionmentioning

confidence: 99%

“…Machines (SVM) [10,13], Decision Trees (DT) [3,10], Naive Bayes (NB) [10], Multi-Layer Perceptron (MLP) [11], Artificial Neural Networks (ANN) [3], or a combination of these techniques [12]. The accuracy of any classification technique will depend on the system requirements covering important areas -type of activities, number of activities, type of sensors, number of sensors, placement of sensors [17], multiple sensor fusion, etc.…”

Section: Introductionmentioning

confidence: 99%

“…Analysis of collected data is generally performed using statistical signal processing involving the primary steps of feature extraction, feature selection and classification [16]. In terms of classification, a review of the literature shows that different machine learning techniques have been used depending on the application area, e.g.…”

Section: Introductionmentioning

confidence: 99%

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On the data analysis for classification of elementary upper limb movements

et al. 2014

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Purpose. Body worn inertial sensors could be used to assess rehabilitation of patients with impaired upper limb motor control by detecting and classifying how many times particular arm movements (exercises) are made during normal activities. We present a systematic exploration to determine such a system.Methods. Kinematic data was collected from 18 healthy subjects using tri-axial inertial sensors (accelerometers and gyroscopes) located at two positions on the dominant arm as four fundamental arm movements were repeated 20 times each. Ten time domain features were extracted from individual and combinations of sensor axes data, and were used to train a classifier. Three different classifiers were investigated: linear discriminant analysis (LDA), quadratic discriminant analysis (QDA) and support vector machine (SVM). Each was verified using a leave-one-subject-out technique for a generalized classification model, and a ten-fold cross validation technique for a personalized classification model.Results. LDA repeatedly gave the better results when using features extracted from individual sensor axes data.When a personalized learning model is used with LDA, only a single tri-axial sensor (accelerometer or gyroscope) is required to classify all four of the upper limb movements with a sensitivity in the range 92-100%, using as few as 6-10 time-domain features. By comparison, the generalized model using LDA exhibited lower sensitivity and generally required more features (12-18), reflecting the greater variability inherent in a training set comprised of more than one individual's data.Conclusions. We demonstrate that body worn inertial sensors can classify elementary arm movements using a low complexity algorithm.

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Section: Sensor Selection and Placementmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

On the data analysis for classification of elementary upper limb movements

et al. 2014

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“…Machines (SVM) [22], [25], Decision Trees (DT) [9], [16], Naive Bayes (NB) [16], Multi-Layer Perceptron (MLP) [17], Artificial Neural Networks (ANN) [9], or a combination of these techniques [15]. Hidden Markov Models (HMM) [10] have been used for recognising common gestures made when interacting with objects used in daily living.…”

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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A Review on the Artificial Intelligence Algorithms for the Recognition of Activities of Daily Living Using Sensors in Mobile Devices

Pires

Marques

García

et al. 2020

Advances in Intelligent Systems and Computing

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Smart environments and mobile devices are two technologies that when combined may allow the recognition of Activities of Daily Living (ADL) and its environments. This paper focuses on the literature review of the existing machine learning methods for the recognition of ADL and its environments, by means of comparison jointly with a proposal of a novel taxonomy in this context. The sensors used for this purpose depends on the nature of the system and the ADL to recognize. The available in the mobile devices are mainly motion, magnetic and location sensors, but the sensors available in the smart environments may have different types. Data acquired from several sensors can be used for the identification of ADL, where the motion, magnetic and location sensors handle the recognition of activities with movement, and the acoustic sensors handle the recognition of activities related with the environment.

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Daily living activity recognition based on statistical feature quality group selection

Cited by 115 publications

References 35 publications

On the data analysis for classification of elementary upper limb movements

On the data analysis for classification of elementary upper limb movements

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

A Review on the Artificial Intelligence Algorithms for the Recognition of Activities of Daily Living Using Sensors in Mobile Devices

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