Conor Hayden, Deirdre Murray, Dermot Geraghty, D. Meldrum, Orla Hardiman, Bruce Murphy
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
{"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":"https://doi.org/10.1115/1.4064583","url":null,"abstract":"\u0000 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":0.0,"publicationDate":"2024-01-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140492671","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Amanda Johnson, Run Ze Gao, Kendal Marriott, Clark R. Dickerson, Monica Maly, Carolyn Ren
We present a novel electronics-free soft robotic knee brace which employs a closed-loop fluidic regenerative (CLFR) system for dynamic unloading in unicompartmental tibiofemoral osteoarthritis (OA). The existing dynamic unloaders are bulky and heavy largely and have low compliance likely due to the use of electrical control box, which is eliminated in the CLFR system. The system consists of a commercial unloading knee brace, a spring-loaded bellow inserted under the heel inside a shoe, a soft-fluidic actuator (bladder), and tubing for fluid transfer. Its novelty lies in the fact that the user's body weight (self-powered) compresses the bellow to provide energy to inflate the air bladder placed at the knee. As a result, the yielded pressure unloads the undesirable forces due to knee OA during the stance phase of gait while strategically applying no forces during the swing phase. The knee bladder contact pressure/force, the system response time, and the durability were evaluated via contact pressure measurements for six systems with varying bellow volumes and either pneumatic or hydraulic configurations. All systems produced safe pressure outputs for human skin within a tested bodyweight range of 60-90 kg. Pneumatic and hydraulic systems achieved 250 ms and 400 ms pressurization response times, respectively. During cyclic loading, pneumatic and hydraulic systems demonstrated less than 1% and ~10% pressure loss, respectively. Overall, the CLFR system created a promising electronics-free solution for dynamically unloading the knee during gait, indicating a potential new paradigm for knee braces.
{"title":"Electronics-Free Soft Robotic Knee Brace for Dynamic Unloading During Gait for Knee Osteoarthritis: A Proof-of-Concept Study","authors":"Amanda Johnson, Run Ze Gao, Kendal Marriott, Clark R. Dickerson, Monica Maly, Carolyn Ren","doi":"10.1115/1.4064249","DOIUrl":"https://doi.org/10.1115/1.4064249","url":null,"abstract":"We present a novel electronics-free soft robotic knee brace which employs a closed-loop fluidic regenerative (CLFR) system for dynamic unloading in unicompartmental tibiofemoral osteoarthritis (OA). The existing dynamic unloaders are bulky and heavy largely and have low compliance likely due to the use of electrical control box, which is eliminated in the CLFR system. The system consists of a commercial unloading knee brace, a spring-loaded bellow inserted under the heel inside a shoe, a soft-fluidic actuator (bladder), and tubing for fluid transfer. Its novelty lies in the fact that the user's body weight (self-powered) compresses the bellow to provide energy to inflate the air bladder placed at the knee. As a result, the yielded pressure unloads the undesirable forces due to knee OA during the stance phase of gait while strategically applying no forces during the swing phase. The knee bladder contact pressure/force, the system response time, and the durability were evaluated via contact pressure measurements for six systems with varying bellow volumes and either pneumatic or hydraulic configurations. All systems produced safe pressure outputs for human skin within a tested bodyweight range of 60-90 kg. Pneumatic and hydraulic systems achieved 250 ms and 400 ms pressurization response times, respectively. During cyclic loading, pneumatic and hydraulic systems demonstrated less than 1% and ~10% pressure loss, respectively. Overall, the CLFR system created a promising electronics-free solution for dynamically unloading the knee during gait, indicating a potential new paradigm for knee braces.","PeriodicalId":506673,"journal":{"name":"Journal of Medical Devices","volume":"56 2","pages":""},"PeriodicalIF":0.0,"publicationDate":"2023-12-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139181899","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
This paper presents a platform for self-learning of cochlear insertion using computer vision in a 3D surrogate model. Self-learning and practice experiences often improve the confidence associated with eventual real-world trials by novice medical trainees. This helps the trainees practice electrode insertion to minimize the effect of suboptimal electrode placement such as incomplete electrode insertion, electrode kinking, and electrode tip fold-over. Although existing mastoid fitting templates improve insertion trajectories, extensive training is still required. Current methods that use cadavers, virtual training, or physical models from reconstruction images are not good enough for training purposes. The model presented here simulates the dimensions, texture, and feel of inserting the electrode into the cochlea. Currently, the temporal bone is not included, hence it is not meant for practicing drilling and other procedures to access the cochlear. The insertion process is observed in real-time using a camera and a Graphical User Interface that not only shows the video feed, but also provides depth, trajectory, and speed measurements. In a trial conducted for medical trainees there was an overall improvement in all four metrics after they were trained on the hardware/software. There was a 14.20% improvement in insertion depth, 44.24% reduction in insertion speed, 52.90% reduction in back-outs, and a 64.89% reduction in kinks/fold-overs. The advantage of this model is that medical trainees can use it as many times as they like, as the whole set-up is easy, economical, and reusable.
{"title":"Design, Development and Validation of a Smart Cochlear 3D-Printed Model to Train ENT Surgeons","authors":"Michala Dauterman, Anita Jeyakumar, Ishwor Gautam, Alisha Mahajan, Sahana Khanna, Ajay Mahajan","doi":"10.1115/1.4064064","DOIUrl":"https://doi.org/10.1115/1.4064064","url":null,"abstract":"This paper presents a platform for self-learning of cochlear insertion using computer vision in a 3D surrogate model. Self-learning and practice experiences often improve the confidence associated with eventual real-world trials by novice medical trainees. This helps the trainees practice electrode insertion to minimize the effect of suboptimal electrode placement such as incomplete electrode insertion, electrode kinking, and electrode tip fold-over. Although existing mastoid fitting templates improve insertion trajectories, extensive training is still required. Current methods that use cadavers, virtual training, or physical models from reconstruction images are not good enough for training purposes. The model presented here simulates the dimensions, texture, and feel of inserting the electrode into the cochlea. Currently, the temporal bone is not included, hence it is not meant for practicing drilling and other procedures to access the cochlear. The insertion process is observed in real-time using a camera and a Graphical User Interface that not only shows the video feed, but also provides depth, trajectory, and speed measurements. In a trial conducted for medical trainees there was an overall improvement in all four metrics after they were trained on the hardware/software. There was a 14.20% improvement in insertion depth, 44.24% reduction in insertion speed, 52.90% reduction in back-outs, and a 64.89% reduction in kinks/fold-overs. The advantage of this model is that medical trainees can use it as many times as they like, as the whole set-up is easy, economical, and reusable.","PeriodicalId":506673,"journal":{"name":"Journal of Medical Devices","volume":"13 2","pages":""},"PeriodicalIF":0.0,"publicationDate":"2023-11-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139262572","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
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