ArticleJMIR neurotechnology2023
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.
Article in JMIR neurotechnology, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 6 papers.
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Who cites it
6 citing papers in PubMed.
- Deep Learning for Automated Tic Detection and Prediction in Tourette Syndrome Using Electromyography and Video.Research square · 2026Article
- The Role of Digital Tools in Meeting the Needs of Adults With Tourette Syndrome: A Human-Centered Design Approach.JMIR formative research · 2026Article
- Applying artificial intelligence in neurodevelopmental disorders management and research.European journal of medical research · 2026Review
- A comparative study of video-based and electromyography-based detection of tics.Clinical parkinsonism & related disorders · 2026Article
- Digital health and Tourette Syndrome: new technological frontiers in diagnosis and management.Frontiers in psychiatry · 2026Review
- Peripheral Thermoregulatory Modulation for Hot Flash Management: Efficacy of Novel Wrist Cooling Device in Cancer Treatment-Induced and Menopausal Vasomotor Symptoms.AACE endocrinology and diabetesArticle
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Authors and funding
3 authors.
Funding
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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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