Thats a unique idea.You should consider the following idea to enhance your idea so that it would impact uniquely: While mobile and arable sensing can capture unique insights into fine-grained activities (such as gestures and limb-based actions) at an individual level, their energy overheads are still prohibitive enough to prevent them from being executed continuously. ,  explore practical alternatives to addressing this challenge-by exploring how cheap infrastructure sensors or information sources (e.g., BLE beacons) can be harnessed with such mobile/arable sensors to provide an effective solution that reduces energy consumption without sacrificing accuracy. The key idea is that many fine-grained activities that  desire to capture are specific to certain location, movement or background context: infrastructure sensors and information sources (e.g., BLE beacons) offer practical and cheap ways to identify such context. ,  first explore how various infrastructure, mobile & arable sensors can be used to identify fine-grained location/movement context (e.g., transiting through a door).  then show, using a couple of illustrative examples (specifically, the detection of ‘switch pressing’ before exiting a room and the identification of ‘water drinking’ after approaching a water cooler) to show that such background context can be predicted, with sufficient accuracy, with sufficient lead time to enable a ‘triggered’ model for mobile/arable sensing of such microscopic, transient gestures and activities. Moreover, such ‘triggered’ sensing also helps to improve the accuracy of such microscopic gesture recognition, by reducing the set of candidate activity labels. Empirical experiments show that  are able to identify 82.2% of switch-pressing and 91.73% of water-drinking activities in a campus lab setting, with a significant reduction in active sensing time (up to 92.9% compared to continuous sensing).
['S Vigneshwaran', 'Sougata Sen', 'Archan Misra', 'Satyadip Chakraborti', 'Rajesh Krishna Balan']
I am writing a research paper on a idea Using infrastructure-provided context filters for efficient fine-grained activity sensing
[{"from": "human", "value": "I am writing a research paper on a idea Using infrastructure-provided context filters for efficient fine-grained activity sensing"}, {"from": "gpt", "value": "Thats a unique idea.You should consider the following idea to enhance your idea so that it would impact uniquely: While mobile and arable sensing can capture unique insights into fine-grained activities (such as gestures and limb-based actions) at an individual level, their energy overheads are still prohibitive enough to prevent them from being executed continuously. ,  explore practical alternatives to addressing this challenge-by exploring how cheap infrastructure sensors or information sources (e.g., BLE beacons) can be harnessed with such mobile/arable sensors to provide an effective solution that reduces energy consumption without sacrificing accuracy. The key idea is that many fine-grained activities that  desire to capture are specific to certain location, movement or background context: infrastructure sensors and information sources (e.g., BLE beacons) offer practical and cheap ways to identify such context. ,  first explore how various infrastructure, mobile & arable sensors can be used to identify fine-grained location/movement context (e.g., transiting through a door).  then show, using a couple of illustrative examples (specifically, the detection of \u2018switch pressing\u2019 before exiting a room and the identification of \u2018water drinking\u2019 after approaching a water cooler) to show that such background context can be predicted, with sufficient accuracy, with sufficient lead time to enable a \u2018triggered\u2019 model for mobile/arable sensing of such microscopic, transient gestures and activities. Moreover, such \u2018triggered\u2019 sensing also helps to improve the accuracy of such microscopic gesture recognition, by reducing the set of candidate activity labels. Empirical experiments show that  are able to identify 82.2% of switch-pressing and 91.73% of water-drinking activities in a campus lab setting, with a significant reduction in active sensing time (up to 92.9% compared to continuous sensing)."}]