Conor Hayden, Deirdre Murray, Dermot Geraghty, D. Meldrum, Orla Hardiman, Bruce Murphy
{"title":"设计和验证用于评估神经系统疾病患者灵活性的新型手戴式传感器","authors":"Conor Hayden, Deirdre Murray, Dermot Geraghty, D. Meldrum, Orla Hardiman, Bruce Murphy","doi":"10.1115/1.4064583","DOIUrl":null,"url":null,"abstract":"\n Sensitive measurement of hand dexterity is important in many neurological conditions such as Stroke, Parkinson's Disease or Amyotrophic Lateral Sclerosis. Current multi-item rating scales and performance-based tests lack sensitivity and contain subjective biases. This paper presents the design and validation of an objective, novel hand worn dexterity measurement device that digitises the Finger Tapping Test (FTT), a widely used test in neurological practice. The device was designed to address predefined user needs and design requirements. It comprises two distinct sections, a mechanical system which attaches to a participant's thumb and index finger and an electronic system which captures/transmits data to a secure cloud storage. The accuracy (for four devices) was validated by plotting the known displacements against the calculated displacements, which returned slopes approximately equal to one. A maximum extension force of 0.51 N was required to extend the cord to 200 mm extension. Clinical testing was carried out on a small sample of heathy people (n=3) and people with Amyotrophic Lateral Sclerosis (n=3). Clean datasets were produced from participant's raw data graphs, from which, new features describing a participant's FTT were extracted. The proposed dexterity device digitises the FTT and provides clean, accurate, sensitive and reliable data","PeriodicalId":506673,"journal":{"name":"Journal of Medical Devices","volume":"2 1","pages":""},"PeriodicalIF":0.0000,"publicationDate":"2024-01-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"Design and Validation of a Novel Hand Worn Sensor for Assessment of Dexterity in Neurological Conditions\",\"authors\":\"Conor Hayden, Deirdre Murray, Dermot Geraghty, D. Meldrum, Orla Hardiman, Bruce Murphy\",\"doi\":\"10.1115/1.4064583\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"\\n Sensitive measurement of hand dexterity is important in many neurological conditions such as Stroke, Parkinson's Disease or Amyotrophic Lateral Sclerosis. Current multi-item rating scales and performance-based tests lack sensitivity and contain subjective biases. This paper presents the design and validation of an objective, novel hand worn dexterity measurement device that digitises the Finger Tapping Test (FTT), a widely used test in neurological practice. The device was designed to address predefined user needs and design requirements. It comprises two distinct sections, a mechanical system which attaches to a participant's thumb and index finger and an electronic system which captures/transmits data to a secure cloud storage. The accuracy (for four devices) was validated by plotting the known displacements against the calculated displacements, which returned slopes approximately equal to one. A maximum extension force of 0.51 N was required to extend the cord to 200 mm extension. Clinical testing was carried out on a small sample of heathy people (n=3) and people with Amyotrophic Lateral Sclerosis (n=3). Clean datasets were produced from participant's raw data graphs, from which, new features describing a participant's FTT were extracted. The proposed dexterity device digitises the FTT and provides clean, accurate, sensitive and reliable data\",\"PeriodicalId\":506673,\"journal\":{\"name\":\"Journal of Medical Devices\",\"volume\":\"2 1\",\"pages\":\"\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2024-01-27\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Journal of Medical Devices\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1115/1.4064583\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Journal of Medical Devices","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1115/1.4064583","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
Design and Validation of a Novel Hand Worn Sensor for Assessment of Dexterity in Neurological Conditions
Sensitive measurement of hand dexterity is important in many neurological conditions such as Stroke, Parkinson's Disease or Amyotrophic Lateral Sclerosis. Current multi-item rating scales and performance-based tests lack sensitivity and contain subjective biases. This paper presents the design and validation of an objective, novel hand worn dexterity measurement device that digitises the Finger Tapping Test (FTT), a widely used test in neurological practice. The device was designed to address predefined user needs and design requirements. It comprises two distinct sections, a mechanical system which attaches to a participant's thumb and index finger and an electronic system which captures/transmits data to a secure cloud storage. The accuracy (for four devices) was validated by plotting the known displacements against the calculated displacements, which returned slopes approximately equal to one. A maximum extension force of 0.51 N was required to extend the cord to 200 mm extension. Clinical testing was carried out on a small sample of heathy people (n=3) and people with Amyotrophic Lateral Sclerosis (n=3). Clean datasets were produced from participant's raw data graphs, from which, new features describing a participant's FTT were extracted. The proposed dexterity device digitises the FTT and provides clean, accurate, sensitive and reliable data