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Thumbs-Up: Wearable Sensing Device for Detecting Hand-to-Mouth Compulsive Habits

机译:竖起大拇指:可穿戴式传感装置,用于检测手部强迫习惯

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Thumbs-Up explores a novel sensing method for detection of hand-to-mouth compulsive habits. Using electrical bio-impedance spectroscopy and inertial measurement units, a prototype system was implemented. The system can easily be worn around the arm and may perhaps be integrated into future wearable devices. It recognises occurrences of the habits in real-time, allowing monitoring and immediate interventions. These have so far been very limited, impeding behavioural studies and the development of therapeutic treatments. Throughout this paper the method's feasibility is demonstrated and aspects of its performance are explored. We present an approach to process the bio-impedance signals and associate them with possible body postures. A positioning strategy optimises the device's sensitivity and increases its efficacy. Machine learning algorithms are leveraged to infer the hand-to-mouth detection. We achieve 92% detection accuracy for recurrent usage and 90% accuracy for users that have not been previously encountered.
机译:竖起大拇指探讨了一种用于检测手嘴强迫习惯的新型传感方法。使用电气生物阻抗光谱和惯性测量单元,实施了原型系统。系统可以在臂周围容易地佩戴,也许可能集成到未来的可穿戴设备中。它识别实时习惯的出现,允许监控和立即干预。这些迄今为止已经非常有限,阻碍了行为研究和治疗治疗的发展。在本文中,探讨了该方法的可行性,并探讨了其表现的方面。我们提出了一种处理生物阻抗信号的方法,并将它们与可能的身体姿势相关联。定位策略优化设备的灵敏度并提高其功效。机器学习算法被利用以推断出嘴巴检测。我们达到了92%的检测准确性,用于经常使用的使用率和预先遇到的用户的90%的准确性。

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