Activity recognition and step detection with smartphones: Towards terminal based indoor positioning system

Schindhelm, Corina Kim

doi:10.1109/pimrc.2012.6362769

Cited by 25 publications

(11 citation statements)

References 7 publications

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“…The impact of the window size on the three body positions using the KNN and DT classifier are presented in Figure 8. As can be seen, most of the time (14,16,18,20, and 22 s windows), the results were improved at all body positions with both classifiers, specifically for STB activity. The important improvement for STB activity was about more than 30% at the RH and 22% at the LH body positions using KNN, while less improvement was observed at the TP position, at about 7%, because it already had a high accuracy at the minimum window size.…”

Section: Influence Of Window Size On the Recognition Of Activitiesmentioning

confidence: 81%

SARM: Salah Activities Recognition Model Based on Smartphone

et al. 2019

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Alzheimer's is a chronic neurodegenerative disease that frequently occurs in many people today. It has a major effect on the routine activities of affected people. Previous advancement in smartphone sensors technology enables us to help people suffering from Alzheimer's. For people in the Muslim community, where it is mandatory to offer prayers five times a day, it may mean that they are struggling in their daily life prayers due to Alzheimer's or lack of concentration. To deal with such a problem, automated mobile sensor-based activity recognition applications can be supportive to design accurate and precise solutions with an objective to direct the Namazi (worshipper). In this paper, a Salah activities recognition model (SARM) using a mobile sensor is proposed with the aim to recognize specific activities, such as Al-Qayam (standing), Ruku (standing to bowing), and Sujud (standing to prostration). This model entails the collection of data, selection and placement of sensor, data preprocessing, segmentation, feature extraction, and classification. The proposed model will provide a stepping edge to develop an application for observing prayer. For these activities' recognition, data sets were collected from ten subjects, and six different features sets were used to get improved results. Extensive experiments were performed to test and validate the model features to train random forest (RF), K-nearest neighbor (KNN), naive Bayes (NB), and decision tree (DT). The predicted average accuracy of RF, KNN, NB, and DT was 97%, 94%, 71.6%, and 95% respectively.

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Section: Influence Of Window Size On the Recognition Of Activitiesmentioning

confidence: 81%

SARM: Salah Activities Recognition Model Based on Smartphone

et al. 2019

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“…So it's crucial to set a suitable window length. Generally, the window length is set to a constant [13]. Some works also choose the mean of several previous windows as the current window length.…”

Section: ) Scoring Algorithmmentioning

confidence: 99%

Design of Counting-Step Algorithm for Smartphone-Based Indoor Localization

Song¹,

Liu²,

Huang³

et al. 2017

dtcse

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Abstract. In recent years, the smartphones which are integrated with many high-accuracy sensors have become very popular. Utilizing the development of sensors, developers have done many useful researchers. Indoor positioning navigation is an interesting and hot field of them.Step detecting and counting are key technologies in indoor positioning. In this paper, we propose a novel method to count steps which is an improvement of the classical steps detection method, the peak detection method of the acceleration. Our proposed scheme consists of two parts: the scoring part and adaptive window length part. Two adaptive window length algorithms which can adapt to the varying velocity are also proposed in this paper. In our experiments, a smartphone -Nubia NX513J is handled, with screen facing upwards to record the accelerometer data. The scoring algorithm of acceleration in the process of step detection is tested in two kinds of path: a straight path and a U-shaped path in which it shows better results than conventional peak detection method.

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“…Still, all techniques have certain shortcomings. In previous works we tried to detect walking patterns and steps via Smart phones [23] and also tried to calculate paths via the kinect (camera and laser based approach) [4]. For this work we focused again on a Smart phone based approach combined with a marker.…”

Section: A Walking Pathsmentioning

confidence: 99%

Community based approach for generating building layouts

Schindhelm

Linnhoff‐Popien²,

Janus³

2013

International Conference on Indoor Positioning and Indoor Navigation

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In this paper we propose the design and evaluation of an algorithm capable of inferring basic floor plans from motion paths generated from smartphone sensors. First we shortly discuss the applicability of modern smartphones equipped with various sensors to create motion paths through premises. Some dead reckoning approaches which are used to track and calculate the position of devices are mentioned. After that we present an algorithm which shall use motion paths for the creation of maps holding probabilistic information regarding the accessibility of specific areas within a confined structure such as a building. The algorithm thus differs from traditional SLAM and mapping approaches in that it explicitly focuses on map generation and path interpretation and does not implement any sort of sensorspecific processing. In the concluding evaluation it is shown that the presented algorithm while still prone to certain types of bad input data can calculate free space from the motion paths with a sufficient accuracy for basic mapping tasks, especially if more input samples are used. Finally, various possible improvements are discussed which may improve the algorithms processing quality and robustness.

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Activity recognition and step detection with smartphones: Towards terminal based indoor positioning system

Cited by 25 publications

References 7 publications

SARM: Salah Activities Recognition Model Based on Smartphone

SARM: Salah Activities Recognition Model Based on Smartphone

Design of Counting-Step Algorithm for Smartphone-Based Indoor Localization

Community based approach for generating building layouts

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