Sensor Applications for Human Activity Recognition in Smart Environments
Biying Fu
2021
Human activity recognition (HAR) is the automated recognition of individual or group activities from sensor inputs. It deals with a wide range of application areas, such as for health care, assisting technologies, quantified-self and safety applications. HAR is the key to build human-centred applications and enables users to seamlessly and naturally interact with each other or with a smart environment. A smart environment is an instrumented room or space equipped with sensors and actuators to
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... rceive the physical state or human activities within this space. The diversity of sensors makes it difficult to use the appropriate sensor to build specific applications. This work aims at presenting sensor-driven applications for human activity recognition in smart environments by using novel sensing categories beyond the existing sensor technologies commonly applied to these tasks. The intention is to improve the interaction for various sub-fields of human activities. Each application addresses the difficulties following the typical process pipeline for designing a smart environment application. At first, I survey most prominent research works with focus on sensor-driven categorization in the research domain of HAR to identify possible research gaps to position my work. I identify two use-cases: quantified-self and smart home applications. Quantified-self aims at self-tracking and self-knowledge through numbers. Common sensor technology for daily tracking of various aerobic endurance training activities, such as walking, running or cycling are based on acceleration data with wearable. However, more stationary exercises, such as strength-based training or stretching are also important for a healthy life-style, as they improve body coordination and balance. These exercises are not well tracked by wearing only a single wearable sensor, as these activities rely on coordinated movement of the entire body. I leverage two sensing categories to design two portable mobile applications for remote sensing of these more stationary e [...]
doi:10.26083/tuprints-00017485
fatcat:qjlbo2ybnfh7vecrand5hqv3jy