Towards unsupervised physical activity recognition using smartphone accelerometers

Lu, Yonggang; Ye, Wei; Liu, Li; Zhong, Jun; Sun, Letian; Liu, Ye

doi:10.1007/s11042-015-3188-y

Cited by 197 publications

(67 citation statements)

References 41 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…signals with wavelet transform and Fourier transform) [2], and symbolic representation [3]. Classification methods, such as decision trees, k-Nearest Neighbour (k-NN), and Support Vector Machines (SVM), are then trained to identify different activities using the handcrafted features [4]- [6]. To further improve recognition accuracy, some researchers have demonstrated that ensemble classification methods, which combine multiple learning algorithms together, can achieve better outcomes in some cases.…”

Section: Related Workmentioning

confidence: 99%

See 1 more Smart Citation

Deep learning for human activity recognition: A resource efficient implementation on low-power devices

Ravì

Wong

et al. 2016

2016 IEEE 13th International Conference on Wearable and Implantable Body Sensor Networks (BSN)

203

113

View full text Add to dashboard Cite

Abstract-Human Activity Recognition provides valuable contextual information for wellbeing, healthcare, and sport applications. Over the past decades, many machine learning approaches have been proposed to identify activities from inertial sensor data for specific applications. Most methods, however, are designed for offline processing rather than processing on the sensor node. In this paper, a human activity recognition technique based on a deep learning methodology is designed to enable accurate and real-time classification for low-power wearable devices. To obtain invariance against changes in sensor orientation, sensor placement, and in sensor acquisition rates, we design a feature generation process that is applied to the spectral domain of the inertial data. Specifically, the proposed method uses sums of temporal convolutions of the transformed input. Accuracy of the proposed approach is evaluated against the current state-of-the-art methods using both laboratory and real world activity datasets. A systematic analysis of the feature generation parameters and a comparison of activity recognition computation times on mobile devices and sensor nodes are also presented.

show abstract

Section: Related Workmentioning

confidence: 99%

“…Features, such as mean [1], Fourier transforms [2], and symbols [3], are typically extracted from segments of data and then trained using classification methods [4]- [8]. However, these methods are still limited to the specific classification tasks that they were designed for.…”

Section: Introductionmentioning

confidence: 99%

Deep learning for human activity recognition: A resource efficient implementation on low-power devices

Ravì

Wong

et al. 2016

2016 IEEE 13th International Conference on Wearable and Implantable Body Sensor Networks (BSN)

203

113

View full text Add to dashboard Cite

show abstract

“…To classify activities such as walking, race walking, and running based on unlabeled data, an unsupervised method for recognizing physical activities using smartphone accelerometers has been proposed [32]. Two additional smartphones are attached to the upper arms of the user to recognize specific actions while playing basketball, such as passing or bouncing the ball, or a free throw.…”

Section: Related Workmentioning

confidence: 99%

Heart rate monitoring, activity recognition, and recommendation for e-coaching

Pessemier

Martens

2018

Multimed Tools Appl

View full text Add to dashboard Cite

Equipped with hardware, such as accelerometer and heart rate sensor, wearables enable measuring physical activities and heart rate. However, the accuracy of these heart rate measurements is still unclear and the coupling with activity recognition is often missing in health apps. This study evaluates heart rate monitoring with four different device types: a specialized sports device with chest strap, a fitness tracker, a smart watch, and a smartphone using photoplethysmography. In a state of rest, similar measurement results are obtained with the four devices. During physical activities, the fitness tracker, smart watch, and smartphone measure sudden variations in heart rate with a delay, due to movements of the wrist. Moreover, this study showed that physical activities, such as squats and dumbbell curl, can be recognized with fitness trackers. By combining heart rate monitoring and activity recognition, personal suggestions for physical activities are generated using a tag-based recommender and rule-based filter.

show abstract

“…This model integrates multiple classifiers in accordance with specific integration rules to solve the classification problem and can thus improve the accuracy and generalisation capability of system prediction. Ensemble learning is widely used in various fields, such as human activity recognition [33][34][35]39], human motion tracking [11,32], prediction [36,44], water quality supervision [37,38] and face or emotion discrimination [4,14,20].…”

Section: Introductionmentioning

confidence: 99%

Multi-classifier ensemble based on dynamic weights

Ren

2017

Multimed Tools Appl

View full text Add to dashboard Cite

In this study, a novel multi-classifier ensemble method based on dynamic weights is proposed to reduce the interference of unreliable decision information and improve the accuracy of fusion decision. The algorithm defines decision credibility to describe the realtime importance of the classifier to the current target, combines this credibility with the reliability calculated by the classifier on the training data set and dynamically assigns the fusion weight to the classifier. Compared with other methods, the contribution of different classifiers to fusion decision in acquiring weights is fully evaluated in consideration of the capability of the classifier to not only identify different sample regions but also output decision information when identifying specific targets. Experimental results on public face databases show that the proposed method can obtain higher classification accuracy than that of single classifier and some popular fusion algorithms. The feasibility and effectiveness of the proposed method are verified.

show abstract

Towards unsupervised physical activity recognition using smartphone accelerometers

Cited by 197 publications

References 41 publications

Deep learning for human activity recognition: A resource efficient implementation on low-power devices

Deep learning for human activity recognition: A resource efficient implementation on low-power devices

Heart rate monitoring, activity recognition, and recommendation for e-coaching

Multi-classifier ensemble based on dynamic weights

Contact Info

Product

Resources

About