An Optimized Method for the Limb Tremor Measurement
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Cited by 2 publications
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the global aging phenomenon has increased the number of individuals with age-related neurological movement disorders including parkinson's Disease (pD) and essential tremor (et). pathological Hand tremor (pHt), which is considered among the most common motor symptoms of such disorders, can severely affect patients' independence and quality of life. To develop advanced rehabilitation and assistive technologies, accurate estimation/prediction of nonstationary pHt is critical, however, the required level of accuracy has not yet been achieved. The lack of sizable datasets and generalizable modeling techniques that can fully represent the spectrotemporal characteristics of PHT have been a critical bottleneck in attaining this goal. this paper addresses this unmet need through establishing a deep recurrent model to predict and eliminate the PHT component of hand motion. More specifically, we propose a machine learning-based, assumption-free, and real-time pHt elimination framework, the pHtnet, by incorporating deep bidirectional recurrent neural networks. the pHtnet is developed over a hand motion dataset of 81 ET and PD patients collected systematically in a movement disorders clinic over 3 years. The PHTNet is the first intelligent systems model developed on this scale for pHt elimination that maximizes the resolution of estimation and allows for prediction of future and upcoming sub-movements.Age-related neurological movement disorders such as Parkinson's Disease (PD) and Essential tremor (ET) 1-4 are expected to become more prevalent as the population of seniors over the age of sixty is expected to increase from 962 million in 2017 to 2.1 billion by 2050, and to 3.1 billion in 2100 5 . Pathological Hand Tremor (PHT) is a common upper-limb motor symptom of several age-related neurological movement disorders and is described as involuntary and pseudo-rhythmic movements 6 affecting coordination, targeting, and speed of intended motions 7 .Unlike physiological tremor, which is identified with low amplitude vibrations occurring within the spectral range of 6 to 14 Hz 8 and affects the performance of individuals in high precision tasks such as robotic surgery 9 , PHT represents higher amplitude motion occurring in the broader frequency range of 3-14 Hz 10 . The repetitive and oscillating nature of PHT differentiates itself from other involuntary movement disorders such as chorea, athetosis, ballism, tics, and myoclonus 11 . Upper-limb tremor significantly limits individuals in performing Activities of Daily Livings (ADLs) 12 . Thus, during the last decade, several techniques and technologies have been proposed in both rehabilitation and assistive domains 13,14 to compensate for the involuntary movement while promoting the voluntary component of motion. The accuracy of a tremor compensation technology (such as sophisticated wearable exosuits) relies significantly on the efficacy and spectrotemporal resolution of the algorithm, as inaccurate or slow extraction techniques do not allow for proper compensation. PHT consists o...
the global aging phenomenon has increased the number of individuals with age-related neurological movement disorders including parkinson's Disease (pD) and essential tremor (et). pathological Hand tremor (pHt), which is considered among the most common motor symptoms of such disorders, can severely affect patients' independence and quality of life. To develop advanced rehabilitation and assistive technologies, accurate estimation/prediction of nonstationary pHt is critical, however, the required level of accuracy has not yet been achieved. The lack of sizable datasets and generalizable modeling techniques that can fully represent the spectrotemporal characteristics of PHT have been a critical bottleneck in attaining this goal. this paper addresses this unmet need through establishing a deep recurrent model to predict and eliminate the PHT component of hand motion. More specifically, we propose a machine learning-based, assumption-free, and real-time pHt elimination framework, the pHtnet, by incorporating deep bidirectional recurrent neural networks. the pHtnet is developed over a hand motion dataset of 81 ET and PD patients collected systematically in a movement disorders clinic over 3 years. The PHTNet is the first intelligent systems model developed on this scale for pHt elimination that maximizes the resolution of estimation and allows for prediction of future and upcoming sub-movements.Age-related neurological movement disorders such as Parkinson's Disease (PD) and Essential tremor (ET) 1-4 are expected to become more prevalent as the population of seniors over the age of sixty is expected to increase from 962 million in 2017 to 2.1 billion by 2050, and to 3.1 billion in 2100 5 . Pathological Hand Tremor (PHT) is a common upper-limb motor symptom of several age-related neurological movement disorders and is described as involuntary and pseudo-rhythmic movements 6 affecting coordination, targeting, and speed of intended motions 7 .Unlike physiological tremor, which is identified with low amplitude vibrations occurring within the spectral range of 6 to 14 Hz 8 and affects the performance of individuals in high precision tasks such as robotic surgery 9 , PHT represents higher amplitude motion occurring in the broader frequency range of 3-14 Hz 10 . The repetitive and oscillating nature of PHT differentiates itself from other involuntary movement disorders such as chorea, athetosis, ballism, tics, and myoclonus 11 . Upper-limb tremor significantly limits individuals in performing Activities of Daily Livings (ADLs) 12 . Thus, during the last decade, several techniques and technologies have been proposed in both rehabilitation and assistive domains 13,14 to compensate for the involuntary movement while promoting the voluntary component of motion. The accuracy of a tremor compensation technology (such as sophisticated wearable exosuits) relies significantly on the efficacy and spectrotemporal resolution of the algorithm, as inaccurate or slow extraction techniques do not allow for proper compensation. PHT consists o...
A finger held in the air exhibits microvibrations, which are reduced when it touches a static object. When a finger moves along a surface, the friction between them produces vibrations, which can not be produced with a free-moving finger in the air. With an inertial measurement unit (IMU) capturing such motion characteristics, we demonstrate the feasibility to detect contact between the finger and static objects. We call our technique ActualTouch. Studies show that a single nail-mounted IMU on the index finger provides sufficient data to train a binary touch status classifier (i.e., touch vs. no-touch), with an accuracy above 95%, generalised across users. This model, trained on a rigid tabletop surface, was found to retain an average accuracy of 96% for 7 other types of everyday surfaces with varying rigidity, and in walking and sitting scenarios where no touch occurred. ActualTouch can be combined with other interaction techniques, such as in a uni-stroke gesture recogniser on arbitrary surfaces, where touch status from ActualTouch is used to delimit the motion gesture data that feed into the recogniser. We demonstrate the potential of ActualTouch in a range of scenarios, such as interaction for augmented reality applications, and leveraging daily surfaces and objects for ad-hoc interactions.
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