Lessons on Collecting Data from Autistic Children using Wrist-worn Sensors

被引:2
|
作者
Bell, Maria [1 ]
Robinson, Elise [2 ]
Gilbert, Thomas J. [1 ]
Day, Sally [1 ]
Hamilton, Antonia F. De C. [1 ]
Ward, Jamie A. [3 ]
机构
[1] UCL, London, England
[2] Queensmill Sch, London, England
[3] Goldsmiths Univ London, London, England
关键词
wearable technology; autism spectrum condition; autism; minimally verbal; emotional dysregulation; human-centred design;
D O I
10.1145/3544794.3558478
中图分类号
TP3 [计算技术、计算机技术];
学科分类号
0812 ;
摘要
Autism is a diverse neurodevelopmental condition that has a hugely varying impact of the lives of autistic people. It is only in the last decades that a greater understanding and public awareness of the autism spectrum has come about, in-part thanks to a growing body of research into the condition. Wearable technology offers great promise in furthering autism research by providing an ability to do detailed behavioral analysis in real-life settings, such as in schools, with minimal intrusion. Such work is particularly crucial in exploring behaviours of those with complex needs and intellectual disabilities, a group who traditionally have been under-served. To achieve this there is a need for wearables that are both practical and acceptable to the individuals being studied. This paper presents our findings from a human-centred design approach to developing and deploying wrist-worn sensors among a diverse population of 16 autistic and 12 neurotypical children over a period of several months. Findings and recommendations from this work highlight the need to take both sensory factors and emotional dysregulation into account when designing wearables for autism. Individual aesthetic and social considerations are particularly important for older children. Equally, a period of sensor desensitisation is necessary when working among those with more complex needs.
引用
收藏
页码:6 / 10
页数:5
相关论文
共 50 条
  • [41] Prediction of the Levodopa Challenge Test in Parkinson's Disease Using Data from a Wrist-Worn Sensor
    Khodakarami, Hamid
    Ricciardi, Lucia
    Contarino, Maria Fiorella
    Pahwa, Rajesh
    Lyons, Kelly E.
    Geraedts, Victor J.
    Morgante, Francesca
    Leake, Alison
    Paviour, Dominic
    De Angelis, Andrea
    Horne, Malcolm
    SENSORS, 2019, 19 (23)
  • [42] Estimation of HRV and SpO2 from Wrist-Worn Commercial Sensors for Clinical Settings
    Jarchi, Delaram
    Salvi, Dario
    Velardo, Carmelo
    Mandi, Adam
    Tarassenko, Lionel
    Clifton, David. A.
    2018 IEEE 15TH INTERNATIONAL CONFERENCE ON BIOMEDICAL AND HEALTH INFORMATICS (BHI) AND THE WEARABLE AND IMPLANTABLE BODY SENSOR NETWORKS (BSN), 2018, : 144 - 147
  • [43] Robust Continuous Authentication Using Cardiac Biometrics From Wrist-Worn Wearables
    Zhao, Tianming
    Wang, Yan
    Liu, Jian
    Cheng, Jerry
    Chen, Yingying
    Yu, Jiadi
    IEEE INTERNET OF THINGS JOURNAL, 2022, 9 (12) : 9542 - 9556
  • [44] Fall Detection Using Kinematic Features from a Wrist-Worn Inertial Sensor
    Dhinesh, R.
    Naheem, Minhas
    Khandelwal, Shubham
    Preejith, S. P.
    Joseph, Jayaraj
    Sivaprakasam, Mohanasankar
    2019 IEEE INTERNATIONAL SYMPOSIUM ON MEDICAL MEASUREMENTS AND APPLICATIONS (MEMEA), 2019,
  • [45] Vascular biomarker measurement using wrist-worn tonometer technology
    Sharman, James E.
    Avolio, Alberto
    JOURNAL OF HYPERTENSION, 2018, 36 (11) : 2138 - 2139
  • [46] Development of a wrist-worn calorie monitoring system using bluetooth
    Chika Sugimoto
    Hartaman Ariesanto
    Hiroshi Hosaka
    Ken Sasaki
    Noriyoshi Yamauchi
    Kiyoshi Itao
    Microsystem Technologies, 2005, 11 : 1028 - 1033
  • [47] Suitability Analysis of Wrist-Worn Sensors for Implementing Pedestrian Dead Reckoning Systems
    Enrique Diez, Luis
    Bahillo, Alfonso
    Otegui, Jon
    Otim, Timothy
    IEEE SENSORS JOURNAL, 2018, 18 (12) : 5098 - 5114
  • [48] Motion Artifact Reduction for Wrist-Worn Photoplethysmograph Sensors Based on Different Wavelengths
    Zhang, Yifan
    Song, Shuang
    Vullings, Rik
    Biswas, Dwaipayan
    Simoes-Capela, Neide
    van Helleputte, Nick
    van Hoof, Chris
    Groenendaal, Willemijn
    SENSORS, 2019, 19 (03):
  • [49] Physical Activity Classification Using the GENEA Wrist-Worn Accelerometer
    Zhang, Shaoyan
    Rowlands, Alex V.
    Murray, Peter
    Hurst, Tina L.
    MEDICINE AND SCIENCE IN SPORTS AND EXERCISE, 2012, 44 (04): : 742 - 748
  • [50] Using Wrist-Worn Activity Recognition for Basketball Game Analysis
    Hoelzemann, Alexander
    Van Laerhoven, Kristof
    5TH INTERNATIONAL WORKSHOP ON SENSOR-BASED ACTIVITY RECOGNITION AND INTERACTION (IWOAR 2018), 2018,