Smartphone Motion Mode Recognition

Klein, Itzik; Solaz, Yuval

doi:10.3390/ecsa-4-04929

Cited by 9 publications

(5 citation statements)

References 8 publications

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“…The smartphone can be held in the right or left hand. dataset used in SLR (17 people in [19]) and the recording time is 12 times more than any other dataset used in 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.…”

Section: Frameworkmentioning

confidence: 99%

“…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.…”

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confidence: 99%

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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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Section: Frameworkmentioning

confidence: 99%

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confidence: 99%

Smartphone Location Recognition: A Deep Learning-Based Approach

Klein

2019

Sensors

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“…FilterGyroX, FilterGyroY, and FilterGyroZ are the filtered gyroscope measurements. MagPercentX, MagPercentY, and MagPercentZ were derived from magnetometer measurements based on Equation (7). Table 11 show the validation results.…”

Section: Classification Using Traditional ML Methodsmentioning

confidence: 99%

“…Pedestrian activity recognition is significant in several fields, such as elderly monitoring and transportation modes recognition, because it can help reduce workload and understand people’s behaviors [ 3 ]. In the process of pedestrian navigation, error accumulation is inevitable; diminishing the cumulative error effectively is a challenge for localization systems but is important, because decreased cumulative error improves the localization accuracy [ 7 , 8 ]. In recent years, motion mode recognition can also be used in pedestrian localization system to adjust positioning algorithm to reduce accumulative error.…”

Section: Introductionmentioning

confidence: 99%

Deep Learning-Based Human Activity Real-Time Recognition for Pedestrian Navigation

Zhang

et al. 2020

Sensors

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Several pedestrian navigation solutions have been proposed to date, and most of them are based on smartphones. Real-time recognition of pedestrian mode and smartphone posture is a key issue in navigation. Traditional ML (Machine Learning) classification methods have drawbacks, such as insufficient recognition accuracy and poor timing. This paper presents a real-time recognition scheme for comprehensive human activities, and this scheme combines deep learning algorithms and MEMS (Micro-Electro-Mechanical System) sensors’ measurements. In this study, we performed four main experiments, namely pedestrian motion mode recognition, smartphone posture recognition, real-time comprehensive pedestrian activity recognition, and pedestrian navigation. In the procedure of recognition, we designed and trained deep learning models using LSTM (Long Short-Term Memory) and CNN (Convolutional Neural Network) networks based on Tensorflow framework. The accuracy of traditional ML classification methods was also used for comparison. Test results show that the accuracy of motion mode recognition was improved from 89.9 % , which was the highest accuracy and obtained by SVM (Support Vector Machine), to 90.74 % (LSTM) and 91.92 % (CNN); the accuracy of smartphone posture recognition was improved from 81.60 % , which is the highest accuracy and obtained by NN (Neural Network), to 93.69 % (LSTM) and 95.55 % (CNN). We give a model transformation procedure based on the trained CNN network model, and then obtain the converted . t f l i t e model, which can be run in Android devices for real-time recognition. Real-time recognition experiments were performed in multiple scenes, a recognition model trained by the CNN network was deployed in a Huawei Mate20 smartphone, and the five most used pedestrian activities were designed and verified. The overall accuracy was up to 89.39 % . Overall, the improvement of recognition capability based on deep learning algorithms was significant. Therefore, the solution was helpful to recognize comprehensive pedestrian activities during navigation. On the basis of the trained model, a navigation test was performed; mean bias was reduced by more than 1.1 m. Accordingly, the positioning accuracy was improved obviously, which is meaningful to apply DL in the area of pedestrian navigation to make improvements.

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“…Such apporaches are based on the measurement of step length and heading calculation. The formar is based on ampirical or biomechnical models which are highly affected by the smartphone mode [2]. Hence the importance of a fast and accurate recognition algorithm that can classify between multiple possible smartphone modes.…”

Section: Introductionmentioning

confidence: 99%

Smartphone Mode Recognition During Stairs Motion

Noy

Bernard

Klein

2019

The 6th International Electronic Conference on Sensors and Applications

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Smartphone mode classification is essential to many applications, such as daily life monitoring, healthcare, and indoor positioning. In the latter, it was shown that knowledge of the smartphone location on pedestrians can improve the positioning accuracy. Most of the research conducted in this field is focused on pedestrian motion in a horizontal plane. In this research, we use supervised machine learning techniques to recognize and classify the smartphone mode (text, talk, pocket and swing) while accounting for the movement up and downstairs. We distinguish between the going up and the down motion, each with four different smartphone modes, making eight states in total. This classification is based on the use of an optimal set of sensors that varies according to battery life and the energy consumption of each sensor. The classifier was trained and tested on a dataset constructed from multiple user measurements (total of 94 min) to achieve robustness. This provided an accuracy of more than 90% in the cross validation method and 91.5% if the texting mode is excluded. When considering only stairs motion, regardless of the direction, the accuracy improves to 97%. These results may assist many algorithms, mainly in pedestrian dead reckoning, in improving a variety of challenges such as speed and step length estimation and cumulative error reduction.

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Smartphone Motion Mode Recognition

Cited by 9 publications

References 8 publications

Smartphone Location Recognition: A Deep Learning-Based Approach

Smartphone Location Recognition: A Deep Learning-Based Approach

Deep Learning-Based Human Activity Real-Time Recognition for Pedestrian Navigation

Smartphone Mode Recognition During Stairs Motion

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