In this paper we describe a simple car crash detection algorithm implemented on Android smartphones. The algorithm uses accelerometer sensor and location sensor information which are combined to detect typical patterns of car crash situations. We present technical details of our implementation and first results of an evaluation.
{"title":"Car crash detection on smartphones","authors":"Julia Lahn, Heiko Peter, Peter Braun","doi":"10.1145/2790044.2790049","DOIUrl":"https://doi.org/10.1145/2790044.2790049","url":null,"abstract":"In this paper we describe a simple car crash detection algorithm implemented on Android smartphones. The algorithm uses accelerometer sensor and location sensor information which are combined to detect typical patterns of car crash situations. We present technical details of our implementation and first results of an evaluation.","PeriodicalId":351171,"journal":{"name":"Proceedings of the 2nd international Workshop on Sensor-based Activity Recognition and Interaction","volume":"722 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2015-06-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"130968803","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}
Self-Organizing Maps (SOMs), also known as Self-Organizing Feature Maps, have been used to reduce the complexity of joint kinematic and kinetic data in order to cluster, classify and visualize cyclic motion data. In this paper we describe the results after training SOMs with preprocessed data based on phase registration by dynamic time warping. For validation, we recorded acceleration data of human locomotion varying the treadmill slope, activity (i.e., walking, jogging, running), and whether or not 1.5 kg weights were attached to the ankles. The topological quality of the SOMs after training improved when the phase registration was applied. Furthermore, test (i.e., combination of treadmill slope and type of gait) and subject classification improved, in particular for walking data, when the phase registration was applied for each individual activity. Activity classification improved when the phase registration was calculated from all cycles of our experiments together.
{"title":"Phase registration improves classification and clustering of cycles based on self-organizing maps","authors":"Juan-Carlos Quintana-Duque, D. Saupe","doi":"10.1145/2790044.2790053","DOIUrl":"https://doi.org/10.1145/2790044.2790053","url":null,"abstract":"Self-Organizing Maps (SOMs), also known as Self-Organizing Feature Maps, have been used to reduce the complexity of joint kinematic and kinetic data in order to cluster, classify and visualize cyclic motion data. In this paper we describe the results after training SOMs with preprocessed data based on phase registration by dynamic time warping. For validation, we recorded acceleration data of human locomotion varying the treadmill slope, activity (i.e., walking, jogging, running), and whether or not 1.5 kg weights were attached to the ankles. The topological quality of the SOMs after training improved when the phase registration was applied. Furthermore, test (i.e., combination of treadmill slope and type of gait) and subject classification improved, in particular for walking data, when the phase registration was applied for each individual activity. Activity classification improved when the phase registration was calculated from all cycles of our experiments together.","PeriodicalId":351171,"journal":{"name":"Proceedings of the 2nd international Workshop on Sensor-based Activity Recognition and Interaction","volume":"5 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2015-06-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"131101781","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}
T. Große-Puppendahl, Oskar Bechtold, Lukas Strassel, David Jakob, Andreas Braun, Arjan Kuijper
Safety is a major concern for non-motorized traffic participants, such as cyclists, pedestrians or skaters. Due to their weak nature compared to cars, accidents often lead to serious implications. In this paper, we investigate how additional protection can be achieved with wearable displays attached to a person's arm, leg or back. Different to prior work, we present an extensive study on design considerations for wearable displays in traffic. Based on interviews, experiments, and an online questionnaire with more than 100 participants, we identify potential placements, form factors, and use-cases. These findings enabled us to develop a wearable display system for traffic safety, called beSeen. It can be attached to different parts of the human body, such as arms, legs, or the back. Our device unobtrusively recognizes turn indication gestures, braking, and its placement on the body. We evaluate beSeen's performance and show that it can be reliably used for enhancing traffic safety.
{"title":"Enhancing traffic safety with wearable low-resolution displays","authors":"T. Große-Puppendahl, Oskar Bechtold, Lukas Strassel, David Jakob, Andreas Braun, Arjan Kuijper","doi":"10.1145/2790044.2790059","DOIUrl":"https://doi.org/10.1145/2790044.2790059","url":null,"abstract":"Safety is a major concern for non-motorized traffic participants, such as cyclists, pedestrians or skaters. Due to their weak nature compared to cars, accidents often lead to serious implications. In this paper, we investigate how additional protection can be achieved with wearable displays attached to a person's arm, leg or back. Different to prior work, we present an extensive study on design considerations for wearable displays in traffic. Based on interviews, experiments, and an online questionnaire with more than 100 participants, we identify potential placements, form factors, and use-cases. These findings enabled us to develop a wearable display system for traffic safety, called beSeen. It can be attached to different parts of the human body, such as arms, legs, or the back. Our device unobtrusively recognizes turn indication gestures, braking, and its placement on the body. We evaluate beSeen's performance and show that it can be reliably used for enhancing traffic safety.","PeriodicalId":351171,"journal":{"name":"Proceedings of the 2nd international Workshop on Sensor-based Activity Recognition and Interaction","volume":"41 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2015-06-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"129150333","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}
Activity Recognition is an integral component of ubiquitous computing. Recognizing an activity is a challenging task since activities can be concurrent, interleaved or ambiguous and can consist of multiple actors (which would require parallel activity recognition). This paper investigates how the discriminative nature of Conditional Random Fields (CRF) can be exploited to enhance the accuracy of recognizing activities when compared to that achieved using generative models. It aims to apply CRF to recognize complex activities, analyze the model trained by CRF and evaluate the performance of CRF against existing models using Stochastic Gradient Descent (which is suitable for online learning).
{"title":"Activity recognition using conditional random field","authors":"Megha Agarwal, Peter A. Flach","doi":"10.1145/2790044.2790045","DOIUrl":"https://doi.org/10.1145/2790044.2790045","url":null,"abstract":"Activity Recognition is an integral component of ubiquitous computing. Recognizing an activity is a challenging task since activities can be concurrent, interleaved or ambiguous and can consist of multiple actors (which would require parallel activity recognition). This paper investigates how the discriminative nature of Conditional Random Fields (CRF) can be exploited to enhance the accuracy of recognizing activities when compared to that achieved using generative models. It aims to apply CRF to recognize complex activities, analyze the model trained by CRF and evaluate the performance of CRF against existing models using Stochastic Gradient Descent (which is suitable for online learning).","PeriodicalId":351171,"journal":{"name":"Proceedings of the 2nd international Workshop on Sensor-based Activity Recognition and Interaction","volume":"3 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2015-06-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"127513282","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}
Florian Grützmacher, Johann-Peter Wolff, C. Haubelt
Mobile devices have become ubiquitous, powerful computing devices. While their use scenarios require new input methods, their typical many-core computing architectures allow for new ways to implement these input methods. In this paper the suitability of many-core digital signal processors for online hand gesture recognition is evaluated. To this end, a system consisting of a data glove with three accelerometers and a many-core digital signal processor board is presented. Experiments assess realtime properties in hand gesture recognition on the many-core processing platform.
{"title":"Exploiting thread-level parallelism in template-based gesture recognition with dynamic time warping","authors":"Florian Grützmacher, Johann-Peter Wolff, C. Haubelt","doi":"10.1145/2790044.2790050","DOIUrl":"https://doi.org/10.1145/2790044.2790050","url":null,"abstract":"Mobile devices have become ubiquitous, powerful computing devices. While their use scenarios require new input methods, their typical many-core computing architectures allow for new ways to implement these input methods. In this paper the suitability of many-core digital signal processors for online hand gesture recognition is evaluated. To this end, a system consisting of a data glove with three accelerometers and a many-core digital signal processor board is presented. Experiments assess realtime properties in hand gesture recognition on the many-core processing platform.","PeriodicalId":351171,"journal":{"name":"Proceedings of the 2nd international Workshop on Sensor-based Activity Recognition and Interaction","volume":"19 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2015-06-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"124958630","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}
{"title":"Proceedings of the 2nd international Workshop on Sensor-based Activity Recognition and Interaction","authors":"","doi":"10.1145/2790044","DOIUrl":"https://doi.org/10.1145/2790044","url":null,"abstract":"","PeriodicalId":351171,"journal":{"name":"Proceedings of the 2nd international Workshop on Sensor-based Activity Recognition and Interaction","volume":"65 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"1900-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"132384322","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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