A real-time tracking system for tremor and trajectory estimation in Parkinson’s disease affected patients

Ferrigno, Luigi; Miele, Gianfranco; Milano, F.; Rodio, Angelo; Santoni, Francesco; Angelis, Alessio De; Moschitta, Antonio; Carbone, Paolo; Cerro, Gianni

doi:10.1109/memea49120.2020.9137210

Cited by 11 publications

(11 citation statements)

References 26 publications

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“…This study extends the results in [ 19 ], where the feasibility of adopting the magnetic tracking system for tremor monitoring was investigated by preliminary experiments, and it adopts a hardware setup which was entirely developed by our research group and presented in [ 16 ]. Extensive measurement results are provided on trajectories that emulate tests for movement tremor, rest tremor and tapping.…”

Section: Introductionsupporting

confidence: 68%

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Parkinson’s Disease Patient Monitoring: A Real-Time Tracking and Tremor Detection System Based on Magnetic Measurements

Milano

Cerro

Santoni

et al. 2021

Sensors

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Reliable diagnosis of early-stage Parkinson’s disease is an important task, since it permits the administration of a timely treatment, slowing the progression of the disease. Together with non-motor symptoms, other important signs of disease can be retrieved from the measurement of the movement trajectory and from tremor appearances. To measure these signs, the paper proposes a magnetic tracking system able to collect information about translational and vibrational movements in a spatial cubic domain, using a low-cost, low-power and highly accurate solution. These features allow the usage of the proposed technology to realize a portable monitoring system, that may be operated at home or in general practices, enabling telemedicine and preventing saturation of large neurological centers. Validation is based on three tests: movement trajectory tracking, a rest tremor test and a finger tapping test. These tests are considered in the Unified Parkinson’s Disease Rating Scale and are provided as case studies to prove the system’s capabilities to track and detect tremor frequencies. In the case of the tapping test, a preliminary classification scheme is also proposed to discriminate between healthy and ill patients. No human patients are involved in the tests, and most cases are emulated by means of a robotic arm, suitably driven to perform required tasks. Tapping test results show a classification accuracy of about 93% using a k-NN classification algorithm, while imposed tremor frequencies have been correctly detected by the system in the other two tests.

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Section: Introductionsupporting

confidence: 68%

“…The TX coil was attached on the middle finger. In previous work [ 19 ], an accelerometer was also fixed at the same point, in order to measure the same oscillation amplitude and frequency, so as to validate the MPS measure.…”

Section: Resultsmentioning

confidence: 99%

Parkinson’s Disease Patient Monitoring: A Real-Time Tracking and Tremor Detection System Based on Magnetic Measurements

Milano

Cerro

Santoni

et al. 2021

Sensors

View full text Add to dashboard Cite

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“…There is an ongoing and substantial interest on identifying a system to track and quantitatively measure RT. In 2021 and 2020, Milano et al 55 and Ferrigno et al 56 proposed a tracking system based on magnetic measurements to track the movement of a robotic arm. The work validated a system able to obtain a rapid and real-time diagnosis of PD symptoms.…”

Section: Monitoring and Detecting Devicesmentioning

confidence: 99%

Technological support for people with Parkinson’s disease: a narrative review

Libero

Langiano

Carissimo

et al. 2022

JGG

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“…The ability to choose the sensor's capabilities, metrological features, and noise contribution, and the resolution of the sensing system and kind of motor pathology for the simulation makes the developed simulator a widely applicable tool, since it allows characterizing the algorithms with an arbitrary level of measurement data quality and to generate data for a customizable period of time, avoiding limitations intrinsically present in already available datasets. Specifically, stemming from the authors' experience in PD monitoring [8,9], this paper presents the development and validation of a pathological movement simulator capable of generating motion and tremor data fully compliant with real measurements performed by IMU devices having specified metrological features.…”

Section: Introductionmentioning

confidence: 99%

Development and Assessment of a Movement Disorder Simulator Based on Inertial Data

Carissimo

Cerro

Ferrigno

et al. 2022

Sensors

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The detection analysis of neurodegenerative diseases by means of low-cost sensors and suitable classification algorithms is a key part of the widely spreading telemedicine techniques. The choice of suitable sensors and the tuning of analysis algorithms require a large amount of data, which could be derived from a large experimental measurement campaign involving voluntary patients. This process requires a prior approval phase for the processing and the use of sensitive data in order to respect patient privacy and ethical aspects. To obtain clearance from an ethics committee, it is necessary to submit a protocol describing tests and wait for approval, which can take place after a typical period of six months. An alternative consists of structuring, implementing, validating, and adopting a software simulator at most for the initial stage of the research. To this end, the paper proposes the development, validation, and usage of a software simulator able to generate movement disorders-related data, for both healthy and pathological conditions, based on raw inertial measurement data, and give tri-axial acceleration and angular velocity as output. To present a possible operating scenario of the developed software, this work focuses on a specific case study, i.e., the Parkinson’s disease-related tremor, one of the main disorders of the homonym pathology. The full framework is reported, from raw data availability to pathological data generation, along with a common machine learning method implementation to evaluate data suitability to be distinguished and classified. Due to the development of a flexible and easy-to-use simulator, the paper also analyses and discusses the data quality, described with typical measurement features, as a metric to allow accurate classification under a low-performance sensing device. The simulator’s validation results show a correlation coefficient greater than 0.94 for angular velocity and 0.93 regarding acceleration data. Classification performance on Parkinson’s disease tremor was greater than 98% in the best test conditions.

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A real-time tracking system for tremor and trajectory estimation in Parkinson’s disease affected patients

Cited by 11 publications

References 26 publications

Parkinson’s Disease Patient Monitoring: A Real-Time Tracking and Tremor Detection System Based on Magnetic Measurements

Parkinson’s Disease Patient Monitoring: A Real-Time Tracking and Tremor Detection System Based on Magnetic Measurements

Technological support for people with Parkinson’s disease: a narrative review

Development and Assessment of a Movement Disorder Simulator Based on Inertial Data

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