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SVM versus MAP on accelerometer data to distinguish among locomotor activities executed at different speeds.

Schmid M, Riganti-Fulginei F, Bernabucci I, Laudani A, Bibbo D, Muscillo R, Salvini A, Conforto S - Comput Math Methods Med (2013)

Bottom Line: Dimension reduction was then performed through 2D Sammon's mapping.In the Bayes' approach, the two features were then fed to a Bayes' classifier that incorporates an update rule, while, in the SVM scheme, the ANN was considered as the kernel function of the classifier.Bayes' approach performed slightly better than SVM on both the training set (91.4% versus 90.7%) and the testing set (84.2% versus 76.0%), favoring the proposed Bayes' scheme as more suitable than the proposed SVM in distinguishing among the different monitored activities.

View Article: PubMed Central - PubMed

Affiliation: Department of Engineering, Roma Tre University, Via Vito Volterra 62, 00146 Rome, Italy.

ABSTRACT
Two approaches to the classification of different locomotor activities performed at various speeds are here presented and evaluated: a maximum a posteriori (MAP) Bayes' classification scheme and a Support Vector Machine (SVM) are applied on a 2D projection of 16 features extracted from accelerometer data. The locomotor activities (level walking, stair climbing, and stair descending) were recorded by an inertial sensor placed on the shank (preferred leg), performed in a natural indoor-outdoor scenario by 10 healthy young adults (age 25-35 yrs.). From each segmented activity epoch, sixteen features were chosen in the frequency and time domain. Dimension reduction was then performed through 2D Sammon's mapping. An Artificial Neural Network (ANN) was trained to mimic Sammon's mapping on the whole dataset. In the Bayes' approach, the two features were then fed to a Bayes' classifier that incorporates an update rule, while, in the SVM scheme, the ANN was considered as the kernel function of the classifier. Bayes' approach performed slightly better than SVM on both the training set (91.4% versus 90.7%) and the testing set (84.2% versus 76.0%), favoring the proposed Bayes' scheme as more suitable than the proposed SVM in distinguishing among the different monitored activities.

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Related in: MedlinePlus

Sensor unit placement, and picture of the sensor unit: top side (left) and bottom side (right).
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fig1: Sensor unit placement, and picture of the sensor unit: top side (left) and bottom side (right).

Mentions: Data were collected through a custom-made wireless inertial sensor unit placed on the shank of the subject's preferred leg (see Figure 1); the unit is able to collect acceleration and angular rate data, as it incorporates a triaxial accelerometer (ADXL345, from Analog Devices, Inc.) and a triaxial gyroscope (ITG-3200, from Invensense, Inc.), and it includes a microcontroller (Atmega328 from Atmel Corporation) to collect and sync data from the sensors, and then send them wirelessly to a portable unit through a bluetooth transceiver (WT12, from Bluegiga Technologies Ltd.). For the purposes of this study, just the proximal-to-distal component of the accelerometer sensor was used. Data were collected at a sampling rate of 100 samples/s.


SVM versus MAP on accelerometer data to distinguish among locomotor activities executed at different speeds.

Schmid M, Riganti-Fulginei F, Bernabucci I, Laudani A, Bibbo D, Muscillo R, Salvini A, Conforto S - Comput Math Methods Med (2013)

Sensor unit placement, and picture of the sensor unit: top side (left) and bottom side (right).
© Copyright Policy
Related In: Results  -  Collection

License
Show All Figures
getmorefigures.php?uid=PMC3860084&req=5

fig1: Sensor unit placement, and picture of the sensor unit: top side (left) and bottom side (right).
Mentions: Data were collected through a custom-made wireless inertial sensor unit placed on the shank of the subject's preferred leg (see Figure 1); the unit is able to collect acceleration and angular rate data, as it incorporates a triaxial accelerometer (ADXL345, from Analog Devices, Inc.) and a triaxial gyroscope (ITG-3200, from Invensense, Inc.), and it includes a microcontroller (Atmega328 from Atmel Corporation) to collect and sync data from the sensors, and then send them wirelessly to a portable unit through a bluetooth transceiver (WT12, from Bluegiga Technologies Ltd.). For the purposes of this study, just the proximal-to-distal component of the accelerometer sensor was used. Data were collected at a sampling rate of 100 samples/s.

Bottom Line: Dimension reduction was then performed through 2D Sammon's mapping.In the Bayes' approach, the two features were then fed to a Bayes' classifier that incorporates an update rule, while, in the SVM scheme, the ANN was considered as the kernel function of the classifier.Bayes' approach performed slightly better than SVM on both the training set (91.4% versus 90.7%) and the testing set (84.2% versus 76.0%), favoring the proposed Bayes' scheme as more suitable than the proposed SVM in distinguishing among the different monitored activities.

View Article: PubMed Central - PubMed

Affiliation: Department of Engineering, Roma Tre University, Via Vito Volterra 62, 00146 Rome, Italy.

ABSTRACT
Two approaches to the classification of different locomotor activities performed at various speeds are here presented and evaluated: a maximum a posteriori (MAP) Bayes' classification scheme and a Support Vector Machine (SVM) are applied on a 2D projection of 16 features extracted from accelerometer data. The locomotor activities (level walking, stair climbing, and stair descending) were recorded by an inertial sensor placed on the shank (preferred leg), performed in a natural indoor-outdoor scenario by 10 healthy young adults (age 25-35 yrs.). From each segmented activity epoch, sixteen features were chosen in the frequency and time domain. Dimension reduction was then performed through 2D Sammon's mapping. An Artificial Neural Network (ANN) was trained to mimic Sammon's mapping on the whole dataset. In the Bayes' approach, the two features were then fed to a Bayes' classifier that incorporates an update rule, while, in the SVM scheme, the ANN was considered as the kernel function of the classifier. Bayes' approach performed slightly better than SVM on both the training set (91.4% versus 90.7%) and the testing set (84.2% versus 76.0%), favoring the proposed Bayes' scheme as more suitable than the proposed SVM in distinguishing among the different monitored activities.

Show MeSH
Related in: MedlinePlus