A Novel Activity Detection System Using Plantar Pressure Sensors and Smartphone

Kawsar, Ferdaus; Hasan, Kamrul; Love, Richard R.; Ahamed, Sheikh Iqbal

doi:10.1109/compsac.2015.201

Cited by 25 publications

(9 citation statements)

References 12 publications

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“…According to [24], the Gaussian mixture model (GMM) and the time series shapelets, applied to the accelerometer and gyroscope data, allow the recognition of sitting, standing, walking, and running activities with mean and standard deviation as features, reporting an accuracy of 88.64%. The authors of [25] also used the mean and standard deviation as features for the application of KNN and SVM methods, in order to recognize walking, resting, running, going downstairs, and going upstairs with a reported accuracy higher than 90%.…”

Section: Related Workmentioning

confidence: 99%

Data Fusion on Motion and Magnetic Sensors embedded on Mobile Devices for the Identification of Activities of Daily Living

Pires¹,

García²,

Pombo³

et al. 2017

Preprint

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Several types of sensors have been available in off-the-shelf mobile devices, including motion, magnetic, vision, acoustic, and location sensors. This paper focuses on the fusion of the data acquired from motion and magnetic sensors, i.e., accelerometer, gyroscope and magnetometer sensors, for the recognition of Activities of Daily Living (ADL) using pattern recognition techniques. The system developed in this study includes data acquisition, data processing, data fusion, and artificial intelligence methods. Artificial Neural Networks (ANN) are included in artificial intelligence methods, which are used in this study for the recognition of ADL. The purpose of this study is the creation of a new method using ANN for the identification of ADL, comparing three types of ANN, in order to achieve results with a reliable accuracy. The best accuracy was obtained with Deep Learning, which, after the application of the L2 regularization and normalization techniques on the sensors' data, reports an accuracy of 89.51%.[13] that uses only the accelerometer sensor, this study proposes the creation of two different methods for the recognition of ADL using different number of sensors in order to adapt the method according to the number of sensors available. Firstly, it proposes the fusion of the data acquired from the accelerometer, and the magnetometer sensors. Secondly, it proposes the fusion of the data acquired from the accelerometer, gyroscope, and magnetometer sensors. The ADL proposed for the recognition are running, walking, going upstairs, going downstairs, and standing, consisting this research on the analysis of the performance of three types of ANN, such as Multilayer Perception (MLP) with Backpropagation, Feedforward neural network with Backpropagation, and Deep Learning. A dataset used for this research is composed by the sensors' data acquired in several experiments with people aged between 16 and 60 years old, distinct lifestyles, and a mobile device in the front pocket of their pants, performing the proposed ADL. This research was conducted with the use of three Java libraries, such as Neuroph [14], Encog [15], and DeepLearning4j [16], and different datasets of features, in order to identify the best dataset of features and type of ANN for the recognition of ADL, verifying that the best accuracy for the recognition of ADL with the two different methods proposed was achieved with Deep Learning methods.This paragraph concludes the section 1, and this paper is organized as follows: Section 2 summarizes the literature review for the use of data fusion techniques with accelerometer, gyroscope, and magnetometer sensors; Section 3 presents the methods used on each stage of the architecture proposed. The results obtained are presented in the Section 4, presenting the discussion about these results in the Section 5. The conclusions of this study are presented in the Section 6. Related WorkData fusion techniques may be used with the data acquired from motion and magnetic sensors available in the off-the-shelf mobile devic...

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Section: Related Workmentioning

confidence: 99%

Data Fusion on Motion and Magnetic Sensors embedded on Mobile Devices for the Identification of Activities of Daily Living

Pires¹,

García²,

Pombo³

et al. 2017

Preprint

View full text Add to dashboard Cite

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“…Chen et al have designed a foot-wearable interface for locomotion mode recognition based on contact force distribution [78]. Kawsar et al have developed a novel activity detection system using plantar pressure sensors and a smartphone [79]. Table 3 contains the comparison of these systems.…”

Section: Posture and Activity Recognition And Energy Expenditure Estmentioning

confidence: 99%

“…An example of a pressure sensor signal located at heel for different daily living activities [76] is shown in Figure 3b. All of the systems utilize motion sensors such as accelerometers and the systems in [77,79] have a gyroscope as well.…”

Section: Posture and Activity Recognition And Energy Expenditure Estmentioning

confidence: 99%

“…PC post processing for activity classification using neural network [81], decision trees [74], and support vector machines (SVM) [81] PC post processing for activity classification utilizing multinomial logistic discrimination (MLD) [80] PC post processing for activity classification using decision trees [78] and linear discriminant analysis [78] Four different decision trees to classify activity from four sets of sensors (left shoe, right shoe, accelerometer, and gyroscope. Majority voting to decide the activity [79] Activities [55,82] and healthy subjects [73,81] Initial validation study on five healthy subjects [80] Validated on five healthy subjects and one subject with amputee NA Accuracy~99% 96% 98.4%~99%…”

Section: Posture and Activity Recognition And Energy Expenditure Estmentioning

confidence: 99%

“…The study in [81] also reports that activity prediction models based on multinomial logistic discrimination (MLD) is computationally less expensive in terms of required memory space and execution time, and performs equally well in terms of accuracy, as compared to SVM. Binary decision trees [74,78,79], being a light-weight classifier, can possibly enable the implementation of predictive models on the sensor itself. This can potentially reduce the power consumption at the sensor node that would occur during the wireless connection events for raw data transmission.…”

Section: Posture and Activity Recognition And Energy Expenditure Estmentioning

confidence: 99%

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A Comparative Review of Footwear-Based Wearable Systems

2016

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Abstract:Footwear is an integral part of daily life. Embedding sensors and electronics in footwear for various different applications started more than two decades ago. This review article summarizes the developments in the field of footwear-based wearable sensors and systems. The electronics, sensing technologies, data transmission, and data processing methodologies of such wearable systems are all principally dependent on the target application. Hence, the article describes key application scenarios utilizing footwear-based systems with critical discussion on their merits. The reviewed application scenarios include gait monitoring, plantar pressure measurement, posture and activity classification, body weight and energy expenditure estimation, biofeedback, navigation, and fall risk applications. In addition, energy harvesting from the footwear is also considered for review. The article also attempts to shed light on some of the most recent developments in the field along with the future work required to advance the field.

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