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Multimodal fusion of IMUs and EPS body-worn sensors for scratch recognition

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conference contribution
posted on 2023-06-09, 20:51 authored by Zygimantas Jocys, Arash Pour Yazdan Panah Kermani, Daniel RoggenDaniel Roggen
In order to develop and evaluate the extent to which itching affects a person's daily life, it is useful to develop automated means to recognise the action of scratching. We present an investigation of sensors and algorithms to realise a wearable scratch detection device. We collected a dataset, where each user wore 4 inertial measurement unit (IMU) sensors and one electric potential sensor (EPS). Data were collected from nine users, where each user followed a 40-min protocol, which involved scratching different parts of head, shoulder, and leg, as well as other activities such as walking, drinking water, brushing teeth, and typing to a computer. The dataset contained 813 scratching instances and 5 h 15 min of recorded data. We investigated the trade-offs between the number of devices worn (comfort) and accuracy. We trained the k-NN and random forest algorithms by using between 1 and 5 features per channel. We concluded that a scratch could be detected with 80.7% accuracy by using the random forest algorithm on hand coordinates, which required four devices. However, an f1 score of 70% could be achieved with k-NN with IMU and EPS data, which only required one device. Moreover, the fusion of IMU data with EPS data improved the accuracy and reduced the deviation between the folds. This expanded the state-of-the-art method by opening up new trade-offs between accuracy and comfort for future research


Automated recognition of hair touch and scalp scratching through novel sensing modalities, sensor fusion and machine learning approaches; G2738; UNILEVER; MA-2017-02004N


Publication status

  • Published

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  • Accepted version


PervasiveHealth '20: Proceedings of the 14th EAI International Conference on Pervasive Computing Technologies for Healthcare


Association for Computing Machinery

Page range


Event name

EAI PervasiveHealth 2020 - 14th EAI International Conference on Pervasive Computing Technologies for Healthcare

Event location

Atlanta, United States

Event type


Event date

October 6-8, 2020

Place of publication

New York, NY, United States



Department affiliated with

  • Engineering and Design Publications

Research groups affiliated with

  • Sensor Technology Research Centre Publications

Full text available

  • Yes

Peer reviewed?

  • Yes


Lana Yarosh, Maia Jacobs, Junehwa Song, Jakob Badram, Kellie Morrisey, Chia-Fang Chung, Rita Orji

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