Using inertial body-worn sensors, we propose a segmentation approach to detect when a user changes actions. We use Adaboost to combine three threshold-based detectors: force/gravity ratios, peaks of autocorrelation, and local minimums of velocity. Experimenting with the CMU Multi-Modal Activity Database, we find that the first two features are the most important, and our combination approach improves performance with an acceptable level of granularity.
{"title":"Inertial Body-Worn Sensor Data Segmentation by Boosting Threshold-Based Detectors","authors":"Yue Shi, Yuanchun Shi, Xia Wang","doi":"10.1109/ISWC.2012.27","DOIUrl":"https://doi.org/10.1109/ISWC.2012.27","url":null,"abstract":"Using inertial body-worn sensors, we propose a segmentation approach to detect when a user changes actions. We use Adaboost to combine three threshold-based detectors: force/gravity ratios, peaks of autocorrelation, and local minimums of velocity. Experimenting with the CMU Multi-Modal Activity Database, we find that the first two features are the most important, and our combination approach improves performance with an acceptable level of granularity.","PeriodicalId":190627,"journal":{"name":"2012 16th International Symposium on Wearable Computers","volume":"11 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2012-06-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"127824312","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Takafumi Watanabe, D. Kamisaka, S. Muramatsu, Hiroyuki Yokoyama
This paper presents the first study to identify a current station in a large subway network using only a pressure sensor. The air pressure in a train is changed by geographical and structural factors such as the difference in elevation and air flow caused by vents etc. This gives us a good clue in locating our present position especially in underground tunnels. We applied this method to the actual data of the air pressure measured in the Tokyo Metro including 9 lines with 192 stations, and achieved 85 % accuracy to infer at which station we are.
{"title":"At Which Station Am I?: Identifying Subway Stations Using Only a Pressure Sensor","authors":"Takafumi Watanabe, D. Kamisaka, S. Muramatsu, Hiroyuki Yokoyama","doi":"10.1109/ISWC.2012.18","DOIUrl":"https://doi.org/10.1109/ISWC.2012.18","url":null,"abstract":"This paper presents the first study to identify a current station in a large subway network using only a pressure sensor. The air pressure in a train is changed by geographical and structural factors such as the difference in elevation and air flow caused by vents etc. This gives us a good clue in locating our present position especially in underground tunnels. We applied this method to the actual data of the air pressure measured in the Tokyo Metro including 9 lines with 192 stations, and achieved 85 % accuracy to infer at which station we are.","PeriodicalId":190627,"journal":{"name":"2012 16th International Symposium on Wearable Computers","volume":"123 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2012-06-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"123583476","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
When studying the use of ambient audio to generate a secure cryptographic shared key among mobile phones, we encounter a misalignment problem for recorded audio data. The diversity in software and hardware causes mobile phones to produce badly-aligned audio chunks. It decreases the identical fraction in audio samples recorded in nearby mobile phones and consequently the common information available to create a secure key. Unless the mobile devices are real-time capable, this problem can not be solved with standard distributed time synchronisation approaches. We propose a pattern-based approximative matching process to achieve synchronisation independently on each device. Our experimental results show that this method can help to improve the similarity of the audio fingerprints, which are the source to create the communication key.
{"title":"Pattern-Based Alignment of Audio Data for Ad Hoc Secure Device Pairing","authors":"Ngu Nguyen, S. Sigg, An Huynh, Yusheng Ji","doi":"10.1109/ISWC.2012.14","DOIUrl":"https://doi.org/10.1109/ISWC.2012.14","url":null,"abstract":"When studying the use of ambient audio to generate a secure cryptographic shared key among mobile phones, we encounter a misalignment problem for recorded audio data. The diversity in software and hardware causes mobile phones to produce badly-aligned audio chunks. It decreases the identical fraction in audio samples recorded in nearby mobile phones and consequently the common information available to create a secure key. Unless the mobile devices are real-time capable, this problem can not be solved with standard distributed time synchronisation approaches. We propose a pattern-based approximative matching process to achieve synchronisation independently on each device. Our experimental results show that this method can help to improve the similarity of the audio fingerprints, which are the source to create the communication key.","PeriodicalId":190627,"journal":{"name":"2012 16th International Symposium on Wearable Computers","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2012-06-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"127115946","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
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