Mobile Online Activity Recognition System Based on Smartphone Sensors

Lu, Dang-Nhac; Nguyen, Thu-Trang; Ngo, Thi-Thu-Trang; Nguyen, Thi-Hau; Nguyen, Ha-Nam

doi:10.1007/978-3-319-49073-1_39

Cited by 12 publications

(5 citation statements)

References 12 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Recently, researchers have paid a lot of attention to study various methods of providing assistance and safety awareness to drivers. Indeed, such works primarily fall into the following categories: recognizing vehicle mode (car, bus, train, bike, walking ...) [ 2 , 3 , 4 , 5 , 6 , 7 , 8 , 9 ], identifying driving styles (normal, aggressive, drunken, fatigue, drowsy, inattentive ...) [ 10 , 11 , 12 , 13 , 14 , 15 , 16 ], detecting normal/abnormal driving events (moving, stopping, turning left, turning right, weaving, sudden braking, fast u-turn...) [ 17 , 18 , 19 , 20 , 21 , 22 ], accident detection [ 23 , 24 ], estimating energy consumption and pollution [ 25 ], monitoring road and traffic condition [ 26 , 27 , 28 , 29 , 30 ].…”

Section: Introductionmentioning

confidence: 99%

“…Furthermore, the global status reports on road safety 2015 by WHO also shows that approximately a quarter of all road traffic deaths involve in motorcyclists. However, very few existing works provide driving assistance and safety awareness for motorcyclists [ 4 , 20 , 21 , 22 , 32 ]. Nonetheless, there are certain limitations in such works.…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Vehicle Mode and Driving Activity Detection Based on Analyzing Sensor Data of Smartphones

Nguyen

2018

Sensors

Self Cite

View full text Add to dashboard Cite

In this paper, we present a flexible combined system, namely the Vehicle mode-driving Activity Detection System (VADS), that is capable of detecting either the current vehicle mode or the current driving activity of travelers. Our proposed system is designed to be lightweight in computation and very fast in response to the changes of travelers’ vehicle modes or driving events. The vehicle mode detection module is responsible for recognizing both motorized vehicles, such as cars, buses, and motorbikes, and non-motorized ones, for instance, walking, and bikes. It relies only on accelerometer data in order to minimize the energy consumption of smartphones. By contrast, the driving activity detection module uses the data collected from the accelerometer, gyroscope, and magnetometer of a smartphone to detect various driving activities, i.e., stopping, going straight, turning left, and turning right. Furthermore, we propose a method to compute the optimized data window size and the optimized overlapping ratio for each vehicle mode and each driving event from the training datasets. The experimental results show that this strategy significantly increases the overall prediction accuracy. Additionally, numerous experiments are carried out to compare the impact of different feature sets (time domain features, frequency domain features, Hjorth features) as well as the impact of various classification algorithms (Random Forest, Naïve Bayes, Decision tree J48, K Nearest Neighbor, Support Vector Machine) contributing to the prediction accuracy. Our system achieves an average accuracy of 98.33% in detecting the vehicle modes and an average accuracy of 98.95% in recognizing the driving events of motorcyclists when using the Random Forest classifier and a feature set containing time domain features, frequency domain features, and Hjorth features. Moreover, on a public dataset of HTC company in New Taipei, Taiwan, our framework obtains the overall accuracy of 97.33% that is considerably higher than that of the state-of the art.

show abstract

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Vehicle Mode and Driving Activity Detection Based on Analyzing Sensor Data of Smartphones

Nguyen

2018

Sensors

Self Cite

View full text Add to dashboard Cite

show abstract

“…They also combined the classifiers and found that the combination has better accuracy compared to the single classifier. Lu et al 61 used RF, NB, SVM, and KNN classifiers for activity recognition, and according to them, RF is the preferred classifier. Weiss et al 62 also showed RF to perform better, while Wang et al 63 got better performance with KNN.…”

Section: Background and Related Workmentioning

confidence: 99%

Wearable Internet-of-Things platform for human activity recognition and health care

Iqbal

Ullah

Anwar

et al. 2020

International Journal of Distributed Sensor Networks

View full text Add to dashboard Cite

We propose to perform wearable sensors-based human physical activity recognition. This is further extended to an Internet-of-Things (IoT) platform which is based on a web-based application that integrates wearable sensors, smartphones, and activity recognition. To this end, a smartphone collects the data from wearable sensors and sends it to the server for processing and recognition of the physical activity. We collect a novel data set of 13 physical activities performed both indoor and outdoor. The participants are from both the genders where their number per activity varies. During these activities, the wearable sensors measure various body parameters via accelerometers, gyroscope, magnetometers, pressure, and temperature. These measurements and their statistical are then represented in features vectors that used to train and test supervised machine learning algorithms (classifiers) for activity recognition. On the given data set, we evaluate a number of widely known classifiers such random forests, support vector machine, and many others using the WEKA machine learning suite. Using the default settings of these classifiers in WEKA, we attain the highest overall classification accuracy of 90%. Consequently, such a recognition rate is encouraging, reliable, and effective to be used in the proposed platform.

show abstract

“…Ronao et al [18] proposed a two-stage continuous hidden Markov model (CHMM) approach for the task of activity recognition using accelerometer and gyroscope sensory data gathered from a smartphone. Lu et al [19] designed an efficient and flexible framework for activity recognition based on smartphone sensors, and the proposed method was independent of device placement and orientation. Most similar HAR systems take advantage of smart devices for collecting sensing data and utilize machine learning algorithms (e.g., Support Vector Machine (SVM), Decision Tree (DT), K-Nearest Neighbor (KNN) [20][21][22]) for activity recognition.…”

Section: Human Activity Recognition Systemsmentioning

confidence: 99%

Transition Activity Recognition System Based on Standard Deviation Trend Analysis

Shi

Zuo

Zhang

2020

Sensors

View full text Add to dashboard Cite

With the development and popularity of micro-electromechanical systems (MEMS) and smartphones, sensor-based human activity recognition (HAR) has been widely applied. Although various kinds of HAR systems have achieved outstanding results, there are still issues to be solved in this field, such as transition activities, which means the transitional process between two different basic activities, discussed in this paper. In this paper, we design an algorithm based on standard deviation trend analysis (STD-TA) for recognizing transition activity. Compared with other methods, which directly take them as basic activities, our method achieves a better overall performance: the accuracy is over 80% on real data.

show abstract

Mobile Online Activity Recognition System Based on Smartphone Sensors

Cited by 12 publications

References 12 publications

Vehicle Mode and Driving Activity Detection Based on Analyzing Sensor Data of Smartphones

Vehicle Mode and Driving Activity Detection Based on Analyzing Sensor Data of Smartphones

Wearable Internet-of-Things platform for human activity recognition and health care

Transition Activity Recognition System Based on Standard Deviation Trend Analysis

Contact Info

Product

Resources

About