A Novel System to Monitor Tic Attacks for Tourette Syndrome Using Machine Learning and Wearable Technology: Preliminary Survey Study and Proposal for a New Sensing Device.

JMIR neurotechnology Pub Date : 2023-04-25 eCollection Date: 2023-01-01 DOI:10.2196/43351
Agni Rajinikanth, Davis Kevin Clark, Marianna Evangelia Kapsetaki
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Abstract

Background: Tourette syndrome is a neurological disorder that is characterized by repeated unintentional physical movement and vocal sounds, better known as tics. Cases of mild Tourette can have tics numerous times throughout the day, while severe cases may have tics every 5 to 10 seconds. At certain times, typically during high levels of stress, tics become chained in an incessant, continuous fashion-this is known as a tic attack. Tic attacks incapacitate the patient, rendering it difficult for them to move, perform daily actions, and even communicate with others. Caretakers-usually guardians, family members, or nurses-can help reduce the time tic attacks last with their presence and by providing emotional support to the patient.

Objective: We describe TSBand, a wearable wristband that uses machine learning algorithms and a variety of sensors to monitor for tic attacks and notify caretakers when an attack occurs.

Methods: We conducted a research survey with 70 Tourette patients to determine the usability and functionality of TSBand; internal review board approval was not required.

Results: This study has resulted in a smart wristband prototype that costs US $62.74; it uses movement, heart rate, sweat, and body temperature to detect tic attacks using a hybrid local outlier factoring and regression algorithm. An audio tic attack detection mechanism is also included, using recurrent neural networks, and a manually activated backup button and backup audio mechanism are fitted to alert caretakers on the personalized companion app.

Conclusions: TSBand enables the caretaker to provide support faster and prevent excessive self-harm or injury during the attack. It is an affordable and effective solution, solving a problem that many Tourette patients, often children, face. This study has not had the opportunity to test TSBand with any Tourette patients, and we aim to perform rigorous testing and analysis after grant funding is secured.

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一种利用机器学习和可穿戴技术监测抽动症发作的新系统:一种新型传感装置的初步调查研究和建议。
背景:抽动秽语综合征是一种神经系统疾病,其特征是重复的无意识的身体运动和声音,通常被称为抽搐。轻微的抽动症患者一天中会抽搐多次,而严重的患者可能每5到10秒抽搐一次。在某些时候,通常是在高度紧张的情况下,抽搐会以一种不间断的、持续的方式被连锁起来——这被称为抽搐发作。抽搐发作使患者丧失行为能力,使他们难以移动,进行日常活动,甚至与他人交流。看护人——通常是监护人、家庭成员或护士——可以通过他们的存在和为病人提供情感支持来帮助减少抽搐发作的持续时间。目的:我们描述了TSBand,一种可穿戴腕带,它使用机器学习算法和各种传感器来监测tic攻击,并在攻击发生时通知看护人员。方法:对70例抽动秽语患者进行研究调查,以确定TSBand的可用性和功能性;不需要内部审查委员会的批准。结果:这项研究已经产生了一个智能腕带原型,成本为62.74美元;它使用运动、心率、出汗和体温来检测抽搐发作,使用混合的局部离群因子分解和回归算法。还包括音频攻击检测机制,使用循环神经网络,并安装了手动激活的备份按钮和备份音频机制,以提醒个性化伴侣应用程序上的护理人员。结论:TSBand使护理人员能够更快地提供支持,并防止在攻击期间过度的自我伤害或伤害。这是一种负担得起且有效的解决方案,解决了许多妥瑞氏症患者(通常是儿童)面临的问题。本研究尚未有机会在任何图雷特患者中测试TSBand,我们的目标是在获得资助后进行严格的测试和分析。
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