Gathering Large Scale Human Activity Information Using Mobile Sensor Devices

Hattori, Yoshiyuki; Inoue, Sozo; Masaki, Takemori; Hirakawa, Go; Sudo, Osamu

doi:10.1109/bwcca.2010.159

Cited by 8 publications

(4 citation statements)

References 8 publications

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“…The recent popularity of iPhone or Android mobile sensors has simplified the measurement of human activities [1]. In the field of medical services [2], for example, they can obtain data that are relevant to lifestyle-related diseases, which may be useful for their prevention [3].…”

Section: Related Workmentioning

confidence: 99%

Room exit recognition using mobile accelerometers and illuminometers

Isoda

Inoue

Kutsuna³

et al. 2014

Proceedings of the 2014 ACM International Joint Conference on Pervasive and Ubiquitous Computing: Adjunct Publication

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At present, office entrances and exits are controlled mainly using RFID tags, such as employee ID cards or admission cards. When using an RFID tag, the card reader is placed at the entrance and recognition only occurs when entering the room in most cases. Thus, the information required to enter is only recognized during entry but not when leaving. In this paper, we propose a method for exit recognition that uses an illuminometer and an accelerometer embedded in a mobile sensor, which assesses the changes in the illuminance and acceleration data for subjects. We analyze the walking data obtained from the feature values of the acceleration data and the exit data derived from the feature values of the illuminance. We found that this method achieves 87.60% of accuracy for exit recognition.

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Section: Related Workmentioning

confidence: 99%

Room exit recognition using mobile accelerometers and illuminometers

Isoda

Inoue

Kutsuna³

et al. 2014

Proceedings of the 2014 ACM International Joint Conference on Pervasive and Ubiquitous Computing: Adjunct Publication

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“…In [5,2,14], big data was generated using their own study reaching a high number of subjects [5], a high variety of sensor orientations [2], and a high variety of sensor types [14]. Besides using only the own acquired data, researchers should consider database fusion strategies to further increase the amount of data and to exploit the advantages of different databases.…”

Section: Database Fusionmentioning

confidence: 99%

“…In [5], an activity information gathering system was developed using mobile sensor devices with triaxial accelerometers. Inertial data of 170 people during five months were acquired.…”

Section: Introductionmentioning

confidence: 99%

Towards Big Data for Activity Recognition: A Novel Database Fusion Strategy

Schuldhaus

Leutheuser

Eskofier

2014

Proceedings of the 9th International Conference on Body Area Networks

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Activity recognition is mandatory in order to provide feedback about the individual quality of life. Usually, activity recognition algorithms are evaluated on one specific database which is limited in the number of subjects, sensors and type of activities. In this paper, a novel database fusion strategy was proposed which fused three different publicly available databases to one large database consisting of 42 subjects. The fusion of databases addresses the two attributes high volume and high variety of the term "big data". Furthermore, an algorithm was developed which can deal with multiple databases varying in the number of sensors and activities. Nine features were computed in sliding windows of inertial data of several sensor positions. Decision-level fusion was performed in order to combine the information of different sensor positions. The proposed classification system achieved an overall mean classification rate of 85.8 % and allows an easy integration of new databases. Using big data is necessary to develop robust and stable activity recognition algorithms in the future.

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“…There is already a work for gathering large-scale activity information by using the previous ALKAN. (Hattori et al, 2010). ALKAN can collect a large amount of accelerometer data and label data from mobile device such as iPhone/iPod Touch to the server.…”

Section: Introductionmentioning

confidence: 99%

Activity Information Sharing System With Video and Acceleration Data

Hirakawa¹,

Hattori²,

Nakamura³

et al. 2011

Proceedings of the 1st International Conference on Pervasive and Embedded Computing and Communication Systems

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In this paper, we introduce the large-scale activity information sharing system ALKAN2. ALKAN2 gathers users' activity information, which consists of three-axis accelerometer and video data using smart phones and web service. The information can be shared with other users, and their evaluations can also be gathered. In designing ALKAN2, we challenged 1) to diversify the types of activities, 2) to gather massive amount of activities, 3) and to motivate users to provide activity information.

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Gathering Large Scale Human Activity Information Using Mobile Sensor Devices

Cited by 8 publications

References 8 publications

Room exit recognition using mobile accelerometers and illuminometers

Room exit recognition using mobile accelerometers and illuminometers

Towards Big Data for Activity Recognition: A Novel Database Fusion Strategy

Activity Information Sharing System With Video and Acceleration Data

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