Shaka: User Movement Estimation Considering Reliability, Power Saving, and Latency Using Mobile Phone

Kobayashi, Arei; Muramatsu, Shigeki; Kamisaka, Daisuke; Watanabe, T.; Minamikawa, Atsunori; Iwamoto, Takeshi; Yokoyama, Haruki

doi:10.1587/transinf.e94.d.1153

Cited by 11 publications

(9 citation statements)

References 19 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…We consider a user’s lifelog to be an individual’s behavior or information observed by a smartphone alone (i.e., without any other special devices). Various studies have focused on lifelogs observed by smartphones (Kobayashi et al 2011; Kwapisz et al 2010; Lee and Cho 2011). In these studies, systems inferred the user’s movement states by using analysis and classification of values observed via a three-dimensional accelerometer.…”

Section: Related Workmentioning

confidence: 99%

“…In these studies, systems inferred the user’s movement states by using analysis and classification of values observed via a three-dimensional accelerometer. In particular, in Kobayashi et al (2011), successfully inferred how a user moves, including walking, running, riding a bicycle, stopping, driving a car, riding a bus, and riding a train. Further, we can also observe a user’s state via new activity recognition APIs on Android by Google.…”

Section: Related Workmentioning

confidence: 99%

See 1 more Smart Citation

An experimental result of estimating an application volume by machine learning techniques

2015

View full text Add to dashboard Cite

In this study, we improved the usability of smartphones by automating a user’s operations. We developed an intelligent system using machine learning techniques that periodically detects a user’s context on a smartphone. We selected the Android operating system because it has the largest market share and highest flexibility of its development environment. In this paper, we describe an application that automatically adjusts application volume.Adjusting the volume can be easily forgotten because users need to push the volume buttons to alter the volume depending on the given situation. Therefore, we developed an application that automatically adjusts the volume based on learned user settings. Application volume can be set differently from ringtone volume on Android devices, and these volume settings are associated with each specific application including games. Our application records a user’s location, the volume setting, the foreground application name and other such attributes as learning data, thereby estimating whether the volume should be adjusted using machine learning techniques via Weka.Electronic supplementary materialThe online version of this article (doi:10.1186/s40064-015-0791-3) contains supplementary material, which is available to authorized users.

show abstract

Section: Related Workmentioning

confidence: 99%

Section: Related Workmentioning

confidence: 99%

An experimental result of estimating an application volume by machine learning techniques

2015

View full text Add to dashboard Cite

show abstract

“…A location that is valuable from educational aspects, and is also a matter of learners' interest (e.g., region (5,8)). A location where learning occurs as educators expect.…”

Section: Pattern 1: a Location Where Learners Were Interested And Edumentioning

confidence: 99%

“…This has recently been extended to the ability of consumer devices such as mobile phones to sense behaviors [5,8]. For example, by recognizing "slipping on a floor" [14], it is possible to estimate a dangerous situation caused by careless behavior.…”

Section: Introductionmentioning

confidence: 99%

Understanding spatial contexts of the real world under explicit or tacit roles of location

Okada

Tada

2013

Proceedings of the 25th Australian Computer-Human Interaction Conference: Augmentation, Application, Innovation, Collaboration

View full text Add to dashboard Cite

To realize context-aware services in a ubiquitous computing environment, it is essential to extract the spatial structure of non-linguistic context information that involves a person at a certain location in the real world. With the techniques of multimodal behavior observation and knowledge science, we propose a method of location-based activity analysis to extract the explicit or tacit roles of location in human activities and to express the spatial contexts of the world. We confirmed that location-based estimation of real-world spatial contexts enables a close approximation of the contexts of real-world people. We also showed that spatial contexts can be multidirectionally understood by accumulating, comparing, and integrating the results of spatial interpretations made by people with different attributes and different viewpoints. The estimated spatial contexts can be used for onsite and offsite support services that consider the inherent characteristics of each location and potential human needs arising at each location.

show abstract

“…Recent advances in machine learning and deep learning have enabled us to infer many things, such as the activity of the user of the sensor device, from information obtained from sensors [1], [2]. We can expect that the improvement in identification and prediction based on sensed data contributes to many things.…”

Section: Introductionmentioning

confidence: 99%

Person Identification Based on Accelerations Sensed in Smartphones with LSTM

Takahashi

Nakamura

Kamiyama

et al. 2021

Journal of Information Processing

View full text Add to dashboard Cite

User identification is an important task for a variety of purposes such as authentication or providing personalized advice for improving user experience. In this paper, we propose a method for identifying a user who is holding a smartphone out of previously given target users from acceleration data obtained from the accelerometer in the smartphone using a deep neural network. This proposed method preliminary creates the model from the acceleration data of each user while walking in its training phase. This method identifies the user from acceleration data for identification based on this model in the classification phase. We evaluated the proposed method with the acceleration obtained from the actual eight and twelve users in two aspects, which were identifications including no-decision choice and that without no-decision choice. Our evaluation showed that the proposed method achieved accuracies higher than 95% for two-to five-class identification without no-decision. The proposed method identified the user with no or little false positive in evaluations with "no-decision.

show abstract

Shaka: User Movement Estimation Considering Reliability, Power Saving, and Latency Using Mobile Phone

Cited by 11 publications

References 19 publications

An experimental result of estimating an application volume by machine learning techniques

An experimental result of estimating an application volume by machine learning techniques

Understanding spatial contexts of the real world under explicit or tacit roles of location

Person Identification Based on Accelerations Sensed in Smartphones with LSTM

Contact Info

Product

Resources

About