Game play in the sport of basketball tends to combine highly dynamic phases in which the teams strategically move across the field, with specific actions made by individual players. Analysis of basketball games usually focuses on the locations of players at particular points in the game, whereas the capture of what actions the players were performing remains underrepresented. In this paper, we present an approach that allows to monitor players' actions during a game, such as dribbling, shooting, blocking, or passing, with wrist-worn inertial sensors. In a feasibility study, inertial data from a sensor worn on the wrist were recorded during training and game sessions from three players. We illustrate that common features and classifiers are able to recognize short actions, with overall accuracy performances around 83.6% (k-Nearest-Neighbor) and 87.5% (Random Forest). Some actions, such as jump shots, performed well (± 95% accuracy), whereas some types of dribbling achieving low (± 44%) recall.
{"title":"Using Wrist-Worn Activity Recognition for Basketball Game Analysis","authors":"Alexander Hölzemann, Kristof Van Laerhoven","doi":"10.1145/3266157.3266217","DOIUrl":"https://doi.org/10.1145/3266157.3266217","url":null,"abstract":"Game play in the sport of basketball tends to combine highly dynamic phases in which the teams strategically move across the field, with specific actions made by individual players. Analysis of basketball games usually focuses on the locations of players at particular points in the game, whereas the capture of what actions the players were performing remains underrepresented. In this paper, we present an approach that allows to monitor players' actions during a game, such as dribbling, shooting, blocking, or passing, with wrist-worn inertial sensors. In a feasibility study, inertial data from a sensor worn on the wrist were recorded during training and game sessions from three players. We illustrate that common features and classifiers are able to recognize short actions, with overall accuracy performances around 83.6% (k-Nearest-Neighbor) and 87.5% (Random Forest). Some actions, such as jump shots, performed well (± 95% accuracy), whereas some types of dribbling achieving low (± 44%) recall.","PeriodicalId":151070,"journal":{"name":"Proceedings of the 5th International Workshop on Sensor-based Activity Recognition and Interaction","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2018-09-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"115921324","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}
We all want to remain in our own homes and communities as we age, and wish to be proactive in our own health and wellness. However, the challenges of aging and age-related chronic diseases force many older adults into long-term care and assisted living facilities. In many countries, for the first time ever there are more older adults than children. This increase will have a significant impact on our healthcare services and economy around the world, as it is estimated that spending on continuing care for seniors will increase significantly over the next decade. There is growing evidence that technological supports can bring about significant benefits for older adults and in supporting their health, while at the same time improving the cost-effectiveness of health and social services. However, the majority of these devices have not made it to market and suffer from various limitations that make them inappropriate for an older adult to operate effciently and effectively. These limitations include the need for the user to have to learn how to use the device, effort required by the user in the technology operation, and an increased burden on family caregivers to install and operate the devices. In order to ensure that future technologies for aging are useful, new ways of thinking in their designs is required. Disruption in the current technology landscape is needed that will force the way that we think about the design of these technology to change. For example, in recent years these limitations have started to be addressed through the application of more advanced approaches, such as artificial intelligence (AI). This presentation will discuss the notion of disruptive technologies and how we are currently applying this concept is the design of our next generation of technologies to support older adults. New technologies will be presented that are built into the user environment and that use artificial intelligence to ensure that they are zero-effort for the user and their caregivers.
{"title":"We all want to remain in our own homes ...","authors":"Alex Mihailidis","doi":"10.1145/3266157.3266206","DOIUrl":"https://doi.org/10.1145/3266157.3266206","url":null,"abstract":"We all want to remain in our own homes and communities as we age, and wish to be proactive in our own health and wellness. However, the challenges of aging and age-related chronic diseases force many older adults into long-term care and assisted living facilities. In many countries, for the first time ever there are more older adults than children. This increase will have a significant impact on our healthcare services and economy around the world, as it is estimated that spending on continuing care for seniors will increase significantly over the next decade. There is growing evidence that technological supports can bring about significant benefits for older adults and in supporting their health, while at the same time improving the cost-effectiveness of health and social services. However, the majority of these devices have not made it to market and suffer from various limitations that make them inappropriate for an older adult to operate effciently and effectively. These limitations include the need for the user to have to learn how to use the device, effort required by the user in the technology operation, and an increased burden on family caregivers to install and operate the devices. In order to ensure that future technologies for aging are useful, new ways of thinking in their designs is required. Disruption in the current technology landscape is needed that will force the way that we think about the design of these technology to change. For example, in recent years these limitations have started to be addressed through the application of more advanced approaches, such as artificial intelligence (AI). This presentation will discuss the notion of disruptive technologies and how we are currently applying this concept is the design of our next generation of technologies to support older adults. New technologies will be presented that are built into the user environment and that use artificial intelligence to ensure that they are zero-effort for the user and their caregivers.","PeriodicalId":151070,"journal":{"name":"Proceedings of the 5th International Workshop on Sensor-based Activity Recognition and Interaction","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2018-09-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"122125116","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}
Wearable sensors potentially enable for a better and unobtrusive recognition of human activity and the state of rest, sleep, stress and drive the ongoing trend of the quantified self-movement. As an enabling technology, powerful, while yet inexpensive MEMS-Chips (micro-electro-mechanical system) push the penetration of a broad variety of mobile devices. Thereby, these devices gain high interest, not only in terms of general customer products, but also as integrated systems in an industrial context, either way to enable continuous monitoring of complex life processes and workplace situations. Another challenge that research is facing concerns the limited human abilities of interaction in context of mobility and in situations, in which high attention is being demanded. New and alternative ways are needed to be found in order to take advantage of all human capabilities to enable safe and unobtrusive interaction.
{"title":"Proceedings of the 5th International Workshop on Sensor-based Activity Recognition and Interaction","authors":"B. Urban, T. Kirste","doi":"10.1145/3266157","DOIUrl":"https://doi.org/10.1145/3266157","url":null,"abstract":"Wearable sensors potentially enable for a better and unobtrusive recognition of human activity and the state of rest, sleep, stress and drive the ongoing trend of the quantified self-movement. As an enabling technology, powerful, while yet inexpensive MEMS-Chips (micro-electro-mechanical system) push the penetration of a broad variety of mobile devices. Thereby, these devices gain high interest, not only in terms of general customer products, but also as integrated systems in an industrial context, either way to enable continuous monitoring of complex life processes and workplace situations. Another challenge that research is facing concerns the limited human abilities of interaction in context of mobility and in situations, in which high attention is being demanded. New and alternative ways are needed to be found in order to take advantage of all human capabilities to enable safe and unobtrusive interaction.","PeriodicalId":151070,"journal":{"name":"Proceedings of the 5th International Workshop on Sensor-based Activity Recognition and Interaction","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2016-06-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"114736159","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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