A step counter service for Java-enabled devices using a built-in accelerometer

Abstract. This multidisciplinary paper reports on a large-scale field trial, designed and implemented by a group of social scientists, computer scientists and statisticians, of a new smartphone-based app for the promotion of walking in everyday life. The app, bActive, is designed for a more diverse range of users than the typical active-lifestyle app, since it requires neither additional equipment nor a great deal of commitment to exercise. As a result, it can raise awareness of walking and promote walking amongst those with only a casual or hesitant engagement with the topic. The 6-week randomised controlled trial with 22-40 year-old male participants (N=152) indicates that bActive prompted users to increase the amount of walking they did by encouraging them to value and increase walking that is incidental to normal everyday activities. Longitudinal data analysis showed that use of the app increased walking by an average of 64% but did not find any evidence to suggest that the inclusion of comparative social feedback improves the impact of such apps on male participants.

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“…The step counting algorithm is based on that of Mladenov and Mock [30], which treats accelerometer values as graph y-values over time.…”

Section: 12mentioning

confidence: 99%

Walking in the Wild – Using an Always-On Smartphone Application to Increase Physical Activity

Harries

Eslambolchilar

Stride

et al. 2013

Lecture Notes in Computer Science

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“…We apply the peak detection algorithm (Mladenov, 2009) There is one more step than the actual it is. That is because the performer shakes at the corner which causes an added peak.…”

Section: Pedestrian Dead Reckoningmentioning

confidence: 99%

An Improved PDR Indoor Locaion Algorithm Based on Probabilistic Constraints

Yu¹,

Zhang²,

Liu³

et al. 2017

Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci.

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ABSTRACT:In this paper, we proposed an indoor pedestrian positioning method which is probabilistic constrained by "multi-target encounter" when the initial position is known. The method is based on the Pedestrian Dead Reckoning (PDR) method. According to the PDR method of positioning error size and indoor road network structure, the buffer distance is determined reasonably and the buffer centering on the PDR location is generated. At the same time, key nodes are selected based on indoor network. In the premise of knowing the distance between multiple key nodes, the forward distance of pedestrians which entered from different nodes can be calculated and then we sum their distances and compared with the known distance between the key nodes, which determines whether pedestrians meet. When pedestrians meet, each two are seen as a cluster. The algorithm determines whether the range of the intersection of the buffer meet the conditions. When the condition is satisfied, the centre of the intersection area is taken as the pedestrian position. At the same time, based on the angle mutation of pedestrian which caused by the special structure of the indoor staircase, the pedestrian's location is matched to the real location of the key landmark (staircase). Then the cumulative error of the PDR method is eliminated. The method can locate more than one person at the same time, as long as you know the true location of a person, you can also know everyone's real location in the same cluster and efficiently achieve indoor pedestrian positioning.

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“…Turning is detected using peak detection algorithm [22], and the threshold is set to 50 in this paper. The peak detection algorithm is used to find the local maximum or minimum during a period of time [22], the turning detection result by peak detection is shown in Fig. 3.…”

Section: B Activity Detectionmentioning

confidence: 99%

ALIMC: Activity Landmark-Based Indoor Mapping via Crowdsourcing

Zhou

Mao

et al. 2015

IEEE Trans. Intell. Transport. Syst.

104

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Indoor maps are integral to pedestrian navigation systems, an essential element of intelligent transportation systems (ITS). In this paper, we propose ALIMC, i.e., Activity Landmarkbased Indoor Mapping system via Crowdsourcing. ALIMC can automatically construct indoor maps for anonymous buildings without any prior knowledge using crowdsourcing data collected by smartphones. ALIMC abstracts the indoor map using a link-node model in which the pathways are the links and the intersections of the pathways are the nodes, such as corners, elevators, and stairs. When passing through the nodes, pedestrians do the corresponding activities, which are detected by smartphones. After activity detection, ALIMC extracts the activity landmarks from the crowdsourcing data and clusters the activity landmarks into different clusters, each of which is treated as a node of the indoor map. ALIMC then estimates the relative distances between all the nodes and obtains a distance matrix. Based on the distance matrix, ALIMC generates a relative indoor map using the multidimensional scaling technique. Finally, ALIMC converts the relative indoor map into an absolute one based on several reference points. To evaluate ALIMC, we implement ALIMC in an office building. Experiment results show that the 80th percentile error of the mapping accuracy is about 0.8-1.5 m.

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A step counter service for Java-enabled devices using a built-in accelerometer

Cited by 87 publications

References 3 publications

Walking in the Wild – Using an Always-On Smartphone Application to Increase Physical Activity

Walking in the Wild – Using an Always-On Smartphone Application to Increase Physical Activity

An Improved PDR Indoor Locaion Algorithm Based on Probabilistic Constraints

ALIMC: Activity Landmark-Based Indoor Mapping via Crowdsourcing

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