PACP: A Position-Independent Activity Recognition Method Using Smartphone Sensors

Yang, Rong; Wang, Baowei

doi:10.3390/info7040072

Cited by 45 publications

(17 citation statements)

References 29 publications

(36 reference statements)

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“…Examination of the proposed SLR approach on 107 people and 31 h of recorded data. The number of different people who participate in this is dataset is 6 times more than any other dataset used in SLR (17 people in [19]) and the recording time is 12 times more than any other dataset used in Sensors 2020, 20, 214 3 of 20 SLR (about 160 min in [22]). This dataset was partly generated for this research and partly uses other publicly available datasets created for other applications but are suitable for the SLR task.…”

mentioning

confidence: 99%

Smartphone Location Recognition: A Deep Learning-Based Approach

Klein

2019

Sensors

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One of the approaches for indoor positioning using smartphones is pedestrian dead reckoning. There, the user step length is estimated using empirical or biomechanical formulas. Such calculation was shown to be very sensitive to the smartphone location on the user. In addition, knowledge of the smartphone location can also help for direct step-length estimation and heading determination. In a wider point of view, smartphone location recognition is part of human activity recognition employed in many fields and applications, such as health monitoring. In this paper, we propose to use deep learning approaches to classify the smartphone location on the user, while walking, and require robustness in terms of the ability to cope with recordings that differ (in sampling rate, user dynamics, sensor type, and more) from those available in the train dataset. The contributions of the paper are: (1) Definition of the smartphone location recognition framework using accelerometers, gyroscopes, and deep learning; (2) examine the proposed approach on 107 people and 31 h of recorded data obtained from eight different datasets; and (3) enhanced algorithms for using only accelerometers for the classification process. The experimental results show that the smartphone location can be classified with high accuracy using only the smartphone’s accelerometers.

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confidence: 99%

Smartphone Location Recognition: A Deep Learning-Based Approach

Klein

2019

Sensors

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“…The position specified activity classifiers refer to training a classifier for each position, and selecting the activity classifier according to the obtained position information in the first stage [1,5,6]. By contrast, one generalized activity classifier for final HAR takes the features produced by an adjustment technique as input [7]. The adjustment technique narrows the feature difference among smartphone positions.…”

Section: Position Recognition and Position-aware Human Activity Recogmentioning

confidence: 99%

“…Some related works studied position recognition methods while the smartphone users are walking [2][3][4], but without discussing other periodic human activities. The position-ware HAR model is designed to leverage smartphone on-body position information as prior knowledge to improve HAR [1,[5][6][7].…”

Section: Introductionmentioning

confidence: 99%

Context-Aware Human Activity and Smartphone Position-Mining with Motion Sensors

et al. 2019

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Today's smartphones are equipped with embedded sensors, such as accelerometers and gyroscopes, which have enabled a variety of measurements and recognition tasks. In this paper, we jointly investigate two types of recognition problems in a joint manner, e.g., human activity recognition and smartphone on-body position recognition, in order to enable more robust context-aware applications. So far, these two problems have been studied separately without considering the interactions between each other. In this study, by first applying a novel data preprocessing technique, we propose a joint recognition framework based on the multi-task learning strategy, which can reduce computational demand, better exploit complementary information between the two recognition tasks, and lead to higher recognition performance. We also extend the joint recognition framework so that additional information, such as user identification with biometric motion analysis, can be offered. We evaluate our work systematically and comprehensively on two datasets with real-world settings. Our joint recognition model achieves the promising performance of 0.9174 in terms of F1-score for user identification on the benchmark RealWorld Human Activity Recognition (HAR) dataset. On the other hand, in comparison with the conventional approach, the proposed joint model is shown to be able to improve human activity recognition and position recognition by 5.1% and 9.6% respectively.

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“…Most of these works use wearable accelerometers, or accelerometer sensors of smartphones. It is evident that works are done in different directions, not only detecting detailed daily life activities and fall [7,19], but also on online activity recognition [20], publishing benchmark datasets [21] as well as analyzing different usage behaviour as in [18,22]. Smartphone placed in pocket Accelerometer Achieved 90% accuracy, uses J48, logistic regression, multi layer perceptron.…”

Section: Related Workmentioning

confidence: 99%

“…Several sensors are used for activity recognition in order to make the recognition system device independent in [17]. A recent work in [18] addresses different usage behavior like smartphones kept at a coat pocket or bag. However, no work could be found that enables detailed activity recognition even when training data is collected using one device at one position (say, right trouser pocket) and activity is recognized for test data collected from a different device kept at the same or different position (say, shirt pocket).…”

Section: Introductionmentioning

confidence: 99%

An Ensemble of Condition Based Classifiers for Device Independent Detailed Human Activity Recognition Using Smartphones †

Saha

Chowdhury

et al. 2018

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Human activity recognition is increasingly used for medical, surveillance and entertainment applications. For better monitoring, these applications require identification of detailed activity like sitting on chair/floor, brisk/slow walking, running, etc. This paper proposes a ubiquitous solution to detailed activity recognition through the use of smartphone sensors. Use of smartphones for activity recognition poses challenges such as device independence and various usage behavior in terms of where the smartphone is kept. Only a few works address one or more of these challenges. Consequently, in this paper, we present a detailed activity recognition framework for identifying both static and dynamic activities addressing the above-mentioned challenges. The framework supports cases where (i) dataset contains data from accelerometer; and the (ii) dataset contains data from both accelerometer and gyroscope sensor of smartphones. The framework forms an ensemble of the condition based classifiers to address the variance due to different hardware configuration and usage behavior in terms of where the smartphone is kept (right pants pocket, shirt pockets or right hand).The framework is implemented and tested on real data set collected from 10 users with five different device configurations. It is observed that, with our proposed approach, 94% recognition accuracy can be achieved.

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PACP: A Position-Independent Activity Recognition Method Using Smartphone Sensors

Cited by 45 publications

References 29 publications

Smartphone Location Recognition: A Deep Learning-Based Approach

Smartphone Location Recognition: A Deep Learning-Based Approach

Context-Aware Human Activity and Smartphone Position-Mining with Motion Sensors

An Ensemble of Condition Based Classifiers for Device Independent Detailed Human Activity Recognition Using Smartphones †

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