Automated recognition of hair touch and scalp scratching through novel sensing modalities, sensor fusion and machine learning approaches

计算行为分析旨在识别日常人类活动及其发生的背景，以获得有关用户日常生活，习惯和对潜在干预的反应的可量化信息。在这种情况下，自动检测人们如何触摸他们的身体部位，特别是头发和头皮，在联合利华有重要的应用。该项目旨在建立和验证可穿戴技术，以测量人们对头皮瘙痒的反应，最重要的是，评估个人护理产品在这种情况下的效果。目的是开发一种方法来自动识别头发和头皮触摸/抓挠的动作（a）定义相关类型的相互作用以及作为瘙痒测量的主要定性和定量变量。B）识别现有的和新的传感器模态和活动识别方法，这是最佳的检测挑战。(c)调查识别性能与模态集和设备数量之间的权衡，与成本和用户舒适度相关。(d)评估每种模式组合在现实环境中的适用性。工作计划第1部分：头皮触摸交互和

Computational behaviour analytics aims to recognise everyday human activities and the context in which they occur to obtain quantifiable information about user's daily routines, habits and reactions to potential interventions. Within this context, the automated detection of how people touch parts of their body, specifically hair and scalp have significant applications in Unilever. This project aims to build and validate wearable technology to measure people's reaction to scalp itch and most importantly, asses the effect of personal care products in this context.The aim is to develop an approach to automatically recognise the action of hair and scalp touch/scratching(a) Defining the relevant types of interactions and the main qualitative and quantitative variables which serve as measure for itch.b) Identify existing and novel sensor modalities and activity recognition approaches which are optimal for the detection challenge.(c) Investigate the trade-offs between recognition performance and sets of modalities and number of devices, as relevant for cost and user comfort.(d) Evaluate the suitability of each combination of modalities in a realistic context.Work PlanPart 1: Survey of scalp touch interaction and