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

全球每年约有20万名儿童出生时患有手部和上肢畸形。这些疾病是由多种疾病(例如脑瘫)引起的，需要内科和外科干预。对精细运动技能的可靠评估对于评估这些干预措施和随着时间的推移监测症状至关重要。客观地评估儿童的手和上肢功能是一个挑战。沟通困难意味着无法使用标准方法。以不需要训练、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，特别是在可能无法面对面互动的情况下；就像2020年的新冠肺炎大流行一样。该项目旨在开发一种智能手套，当患者通过与临床医生的电话会议完成任务时，可以读取和记录手的运动和握力特征。将开发软件来帮助临床医生解释数据。该软件将设计成将测量结果映射到临床相关参数，并提供对患者手运动的更详细了解。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。这种手套将被设计为在收集数据时不会干扰患者，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畸形的儿童进行评估。还将探索该设备商业化的机会。快速识别模式使机器学习在简化诊断过程中发挥关键作用，方法是自动检测关键特征并突出那些准备供临床医生检查的区域。它还将有助于更快、更有信心地诊断和分类患者的状态。患有手部和上肢畸形的儿童通常不能充分沟通，这给诊断带来了困难。错误的诊断和低质量的评估可能会导致无效的手术，导致进一步的矫正手术。我们的手套的好处将包括显著改善有关患者功能的信息；节省临床和临床医生的时间；看专家单位的次数；限制患者的痛苦；并最终改善生活质量和功能。

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.