Daily wrist activity classification using a smart band

Nguyen, Nhan Duc; Truong, Phuc Huu; Jeong, Gu-Min

doi:10.1088/1361-6579/aa7c10

Cited by 17 publications

(19 citation statements)

References 17 publications

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“…In this paper, 23 features were extracted from a sliding window of 100 samples data points with 50% overlap from the filtered data. This selection of window size was proven to be the successful solution for activity recognition in a previous work [ 18 ]. The following features, which have been shown to be effective in human activity recognition [ 18 , 19 , 20 , 21 ], are used in the paper: Average Energy (AE) [ 20 , 22 , 23 ]: The energy of each axis of the triaxial acceleration sensor is calculated by summing the squared discrete FFT component magnitudes of the signal in a sliding window.…”

Section: Walking Distance Estimation Based On Wrist Activity Recogmentioning

confidence: 99%

“… Signal Magnitude Area (SMA) [ 20 ]

where n is the size of a sliding window;

and

are sample point the acceleration data on three axes, x , y and z , of the triaxial sensor, respectively. Intensity of Movement (IM) [ 20 , 24 ]:

Mean:

Standard deviation [ 19 , 20 , 22 ]:

Band power and peak power: the band power, which is defined as the power ratio in three frequency ranges (0–0.5 Hz, 0.5–1 Hz, 1–5 Hz), and the peak power, which is defined as the total power of the five dominant frequencies, are also effective features as demonstrated in [ 18 ]. The power in the band frequency from

Hz to

Hz is calculated by the following equation:

where

is the power spectral density of the Fourier transform of the acceleration signal; N is sampling frequency.…”

Section: Walking Distance Estimation Based On Wrist Activity Recogmentioning

confidence: 99%

“…Band power and peak power: the band power, which is defined as the power ratio in three frequency ranges (0–0.5 Hz, 0.5–1 Hz, 1–5 Hz), and the peak power, which is defined as the total power of the five dominant frequencies, are also effective features as demonstrated in [ 18 ]. The power in the band frequency from

Hz to

Hz is calculated by the following equation:

where

is the power spectral density of the Fourier transform of the acceleration signal; N is sampling frequency.…”

Section: Walking Distance Estimation Based On Wrist Activity Recogmentioning

confidence: 99%

“…In the activity classification task, two support vector machine (SVM) classifiers are used to classify five daily wrist activities during walking ( Figure 4 ), since it is robust and highly accurate as demonstrated in other studies [ 18 , 25 ]. The first classifier is a binary SVM; it classifies two classes: swing activity and the other four activities.…”

Section: Walking Distance Estimation Based On Wrist Activity Recogmentioning

confidence: 99%

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A Robust Step Detection Algorithm and Walking Distance Estimation Based on Daily Wrist Activity Recognition Using a Smart Band

Bui

Nguyen

Jeong

2018

Sensors

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Human activity recognition and pedestrian dead reckoning are an interesting field because of their importance utilities in daily life healthcare. Currently, these fields are facing many challenges, one of which is the lack of a robust algorithm with high performance. This paper proposes a new method to implement a robust step detection and adaptive distance estimation algorithm based on the classification of five daily wrist activities during walking at various speeds using a smart band. The key idea is that the non-parametric adaptive distance estimator is performed after two activity classifiers and a robust step detector. In this study, two classifiers perform two phases of recognizing five wrist activities during walking. Then, a robust step detection algorithm, which is integrated with an adaptive threshold, peak and valley correction algorithm, is applied to the classified activities to detect the walking steps. In addition, the misclassification activities are fed back to the previous layer. Finally, three adaptive distance estimators, which are based on a non-parametric model of the average walking speed, calculate the length of each strike. The experimental results show that the average classification accuracy is about 99%, and the accuracy of the step detection is 98.7%. The error of the estimated distance is 2.2–4.2% depending on the type of wrist activities.

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Section: Walking Distance Estimation Based On Wrist Activity Recogmentioning

confidence: 99%

“… Signal Magnitude Area (SMA) [ 20 ]

where n is the size of a sliding window;

and

are sample point the acceleration data on three axes, x , y and z , of the triaxial sensor, respectively. Intensity of Movement (IM) [ 20 , 24 ]:

Mean:

Standard deviation [ 19 , 20 , 22 ]:

Hz to

Hz is calculated by the following equation:

where

is the power spectral density of the Fourier transform of the acceleration signal; N is sampling frequency.…”

Section: Walking Distance Estimation Based On Wrist Activity Recogmentioning

confidence: 99%

Hz to

Hz is calculated by the following equation:

where

is the power spectral density of the Fourier transform of the acceleration signal; N is sampling frequency.…”

Section: Walking Distance Estimation Based On Wrist Activity Recogmentioning

confidence: 99%

Section: Walking Distance Estimation Based On Wrist Activity Recogmentioning

confidence: 99%

See 2 more Smart Citations

A Robust Step Detection Algorithm and Walking Distance Estimation Based on Daily Wrist Activity Recognition Using a Smart Band

Bui

Nguyen

Jeong

2018

Sensors

Self Cite

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“…We completed three diverse assessment situations so as to cover the most normally utilized situations in the past investigations Specifically client with his/her own information. In addition, we assess the acknowledgment execution with four unique sensors: an accelerometer, a spinner, a direct speeding up and a magnetometer and a gyro meter, a magnetometer [4]. The straight speeding up sensor is a virtual sensor, got from the accelerometer by expelling the gravity part.…”

Section: Introductionmentioning

confidence: 99%

Multi Group Based Daily Living Activity Recognition (DLAR) using Advanced Machine Learning Algorithm

2019

IJITEE

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Human activity recognition (HAR) has realized more interest in several research communities given that understanding user activities and behavior help to deliver proactive and personalized services. Different types of noise in wearable sensors data frequently hamper the human activity recognition classification process. Categorically, in this proposed work designs the three-level hierarchical classification structure, i.e., instance based, group and sub-group based and subject based to detect the daily activity of human body motion among activity groups. In correlation with other famous classifiers, for such as Random Forest Tree, J48, Decision Table, Multilayer Perceptron, NaïveBayes, oneR and REPTree (Reduced Error Pruning Tree), etc., thorough experiments on the mHealth dataset (Shimmer2 mHealth Data) demonstrate that group based classification achieves the best classification results, reaching RFT 99.97%. We trained classier in order to estimate accuracy classification based on (gender, age, height, and weight). We applied validation methods to the process, 10-fold cross-validation. For all three classification structure, we achieve high accuracy values for all three classification task

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Fusion of smartphone sensor data for classification of daily user activities

Şengül

Ozcelik

Misra

et al. 2021

Multimed Tools Appl

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New mobile applications need to estimate user activities by using sensor data provided by smart wearable devices and deliver context-aware solutions to users living in smart environments. We propose a novel hybrid data fusion method to estimate three types of daily user activities (being in a meeting, walking, and driving with a motorized vehicle) using the accelerometer and gyroscope data acquired from a smart watch using a mobile phone. The approach is based on the matrix time series method for feature fusion, and the modified Better-than-the-Best Fusion (BB-Fus) method with a stochastic gradient descent algorithm for construction of optimal decision trees for classification. For the estimation of user activities, we adopted a statistical pattern recognition approach and used the k-Nearest Neighbor (kNN) and Support Vector Machine (SVM) classifiers. We acquired and used our own dataset of 354 min of data from 20 subjects for this study. We report a classification performance of 98.32 % for SVM and 97.42 % for kNN.

show abstract

Daily wrist activity classification using a smart band

Abstract: We obtained recognition error rates of approximately 2.7% by applying the proposed method to the experimental dataset.

Cited by 17 publications

References 17 publications

A Robust Step Detection Algorithm and Walking Distance Estimation Based on Daily Wrist Activity Recognition Using a Smart Band

A Robust Step Detection Algorithm and Walking Distance Estimation Based on Daily Wrist Activity Recognition Using a Smart Band

Multi Group Based Daily Living Activity Recognition (DLAR) using Advanced Machine Learning Algorithm

Fusion of smartphone sensor data for classification of daily user activities

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