Development of a Smart Glove for Remote Monitoring of Paediatric Limb Deformities

开发用于远程监测儿童肢体畸形的智能手套

• 批准号: 2601933
• 金额: --
• 依托单位: Aston University
• 依托单位国家: 英国
• 项目类别: Studentship
• 财政年份: 2021
• 资助国家: 英国
• 起止时间: 2021 至 无数据
• 项目状态: 未结题
• 来源: https://gtr.ukri.org/projects?ref=studentship-2601933
• 关键词: Development; Smart; Glove; Remote; Monitoring

Every year approximately 200,000 children are born worldwide with hand and upper limb deformities. These are caused by a wide range of medical conditions (e.g. cerebral palsy) and require medical and surgical interventions. A reliable assessment of fine motor skills is crucial for evaluating these interventions and monitoring the symptoms over time. Objectively assessing hand and upper limb function in children is a challenge. Communication difficulties mean standard methods cannot be used. Recording accurate joint positions from the patient in a way that requires no training, can surface information accurately and will not interfere with results will provide an invaluable tool to diagnose and evaluate patients, especially in situations where face to face interactions may not be possible; as with the 2020 COVID-19 pandemic. This project aims to develop a smart glove, to read and record hand motion and grip characteristics as the patient completes tasks over teleconference with a clinician. Software will be developed to help clinicians interpret the data. The software will be designed to map measurements to clinically relevant parameters and provide more detailed insight into patient's hand movements. Using the software clinicians will be able to remotely perform diagnosis and assess the success of treatments objectively. The glove will be designed as not to interfere with the patient while data is being collected and be simple enough to be used in a natural home environment. The final design will then be assessed on children with upper limb and hand deformities at Birmingham Children's Hospital. Opportunities for commercialisation of the device will also be explored.Recognising patterns quickly gives machine learning a key role in streamlining the diagnosis process by detecting key features automatically and highlighting those areas ready for the clinician to review. It will also facilitate faster and more confident diagnosis and classification of a patient's state. Children with hand and upper limb deformities are generally unable to adequately communicate making diagnosis difficult. Incorrect diagnosis and poor-quality assessments can result in ineffective surgery, leading to further corrective procedures. The benefits of our glove will include significant improvement in information on patient's functioning; savings in clinic and clinician's time; number of visits to specialists' units; limiting distress to patients; and ultimately improved quality of life and functioning.

全球每年约有 200,000 名出生时患有手部和上肢畸形的儿童。这些是由多种医疗状况（例如脑瘫）引起的，需要医疗和手术干预。对精细运动技能的可靠评估对于评估这些干预措施并随着时间的推移监测症状至关重要。客观评估儿童手部和上肢功能是一项挑战。沟通困难意味着无法使用标准方法。以无需培训、可以准确显示信息且不会干扰结果的方式记录患者准确的关节位置，将为诊断和评估患者提供宝贵的工具，特别是在不可能进行面对面互动的情况下；与 2020 年 COVID-19 大流行一样。该项目旨在开发一款智能手套，当患者通过电话会议与临床医生完成任务时，可以读取和记录手部运动和握力特征。将开发软件来帮助临床医生解释数据。该软件将被设计用于将测量结果映射到临床相关参数，并提供对患者手部动作的更详细的了解。使用该软件，临床医生将能够远程进行诊断并客观地评估治疗的成功程度。该手套的设计不会在收集数据时干扰患者，并且足够简单，可以在自然的家庭环境中使用。最终的设计将在伯明翰儿童医院对患有上肢和手部畸形的儿童进行评估。还将探索该设备的商业化机会。快速识别模式使机器学习通过自动检测关键特征并突出显示可供临床医生检查的区域，在简化诊断过程中发挥关键作用。它还将有助于更快、更自信地对患者状态进行诊断和分类。手部和上肢畸形的儿童通常无法充分沟通，导致诊断困难。不正确的诊断和低质量的评估可能会导致手术无效，从而需要进一步的纠正手术。我们手套的好处包括显着改善患者功能信息；节省诊所和临床医生的时间；访问专家单位的次数；减少患者的痛苦；最终改善生活质量和功能。