634 research outputs found
Machine Learning in Tremor Analysis: Critique and Directions
Tremor is the most frequent human movement disorder, and its diagnosis is based on clinical assessment. Yet finding the accurate clinical diagnosis is not always straightforward. Fine-tuning of clinical diagnostic criteria over the past few decades, as well as device-based qualitative analysis, has resulted in incremental improvements to diagnostic accuracy. Accelerometric assessments are commonplace, enabling clinicians to capture high-resolution oscillatory properties of tremor, which recently have been the focus of various machine-learning (ML) studies. In this context, the application of ML models to accelerometric recordings provides the potential for less-biased classification and quantification of tremor disorders. However, if implemented incorrectly, ML can result in spurious or nongeneralizable results and misguided conclusions. This work summarizes and highlights recent developments in ML tools for tremor research, with a focus on supervised ML. We aim to highlight the opportunities and limitations of such approaches and provide future directions while simultaneously guiding the reader through the process of applying ML to analyze tremor data. We identify the need for the movement disorder community to take a more proactive role in the application of these novel analytical technologies, which so far have been predominantly pursued by the engineering and data analysis field. Ultimately, big-data approaches offer the possibility to identify generalizable patterns but warrant meaningful translation into clinical practice. © 2023 The Authors. Movement Disorders published by Wiley Periodicals LLC on behalf of International Parkinson and Movement Disorder Society
Effects of dance therapy on balance, gait and neuro-psychological performances in patients with Parkinson's disease and postural instability
Postural Instability (PI) is a core feature of
Parkinsonâs Disease (PD) and a major cause of falls and disabilities. Impairment of executive functions has been called as an aggravating factor on motor performances. Dance therapy has been shown effective for improving gait and has been suggested as an alternative rehabilitative method.
To evaluate gait performance, spatial-temporal (S-T) gait
parameters and cognitive performances in a cohort of patients with PD and PI modifications in balance after a cycle of dance therapy
Development and Assessment of a Movement Disorder Simulator Based on Inertial Data
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 Parkinsonâs 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 simulatorâs validation results show a correlation coefficient greater than 0.94 for angular velocity and 0.93 regarding acceleration data. Classification performance on Parkinsonâs disease tremor was greater than 98% in the best test conditions
A multimodal dataset of real world mobility activities in Parkinsonâs disease
Parkinsonâs disease (PD) is a neurodegenerative disorder characterised by motor symptoms such as gait dysfunction and postural instability. Technological tools to continuously monitor outcomes could capture the hour-by-hour symptom fluctuations of PD. Development of such tools is hampered by the lack of labelled datasets from home settings. To this end, we propose REMAP (REal-world Mobility Activities in Parkinsonâs disease), a human rater-labelled dataset collected in a home-like setting. It includes people with and without PD doing sit-to-stand transitions and turns in gait. These discrete activities are captured from periods of free-living (unobserved, unstructured) and during clinical assessments. The PD participants withheld their dopaminergic medications for a time (causing increased symptoms), so their activities are labelled as being âonâ or âoffâ medications. Accelerometry from wrist-worn wearables and skeleton pose video data is included. We present an open dataset, where the data is coarsened to reduce re-identifiability, and a controlled dataset available on application which contains more refined data. A use-case for the data to estimate sit-to-stand speed and duration is illustrated
Exploring the Usage of Text-Entry as a Digital Endpoint in Parkinsonâs Disease
Tese de mestrado, InformĂĄtica, 2022, Universidade de Lisboa, Faculdade de CiĂȘnciasNeurodegenerative diseases are a group of diseases characterised by the loss of neurons and tend
to be fatal. The most researched being Parkinsonâs disease, some connections have been established
between this disease and the use of text-entry towards its diagnosis and monitoring. With such scattered
information regarding neurodegenerative diseases and text-entry, a systematic review was carried out
to show which diseases have been researched in that direction, being mainly PD but also MCI and
MS. The main metrics collected were flight time, hold time and pressure. As previous research did not
include clinicians participation towards the design of diagnosing and monitoring tools, this dissertation
went a step further and worked together with clinicians to understand their expectations on data and
its visualisations. Clinicians believe that text-entry does have potential towards the diagnosis and
monitoring of neurodegenerative diseases. Clinicians also provided concepts of interest against recently
suggested metrics, such as apraxia, bradykinesia and dyskinesia. Finally, it was possible to understand
how clinicians would deem to be the best way to view the data for the patientsâ assessments
Deep learning and wearable sensors for the diagnosis and monitoring of Parkinsonâs disease: A systematic review
Parkinsonâs disease (PD) is a neurodegenerative disorder that produces both motor and non-motor complications, degrading the quality of life of PD patients. Over the past two decades, the use of wearable devices in combination with machine learning algorithms has provided promising methods for more objective and continuous monitoring of PD. Recent advances in artificial intelligence have provided new methods and algorithms for data analysis, such as deep learning (DL).
The aim of this article is to provide a comprehensive review of current applications where DL algorithms are employed for the assessment of motor and nonmotor manifestations (NMM) using data collected via wearable sensors. This paper provides the reader with a summary of the current applications of DL and wearable devices for the diagnosis, prognosis, and monitoring of PD, in the hope of improving the adoption, applicability, and impact of both technologies as support tools.
Following PRISMA (Systematic Reviews and Meta-Analyses) guidelines, sixty-nine studies were selected and analyzed. For each study, information on sample size, sensor configuration, DL approaches, validation methods, and results according to the specific symptom under study were extracted and summarized. Furthermore, quality assessment was conducted according to the Transparent Reporting of a multivariable prediction model for Individual Prognosis Or Diagnosis (TRIPOD) method.
The majority of studies (74%) were published within the last three years, demonstrating the increasing focus on wearable technology and DL approaches for PD assessment. However, most papers focused on monitoring (59%) and computer-assisted diagnosis (37%), while few papers attempted to predict treatment response. Motor symptoms (86%) were treated much more frequently than NMM (14%). Inertial sensors were the most commonly used technology, followed by force sensors and microphones. Finally, convolutional neural networks (52%) were preferred to other DL approaches, while extracted features (38%) and raw data (37%) were similarly used as input for DL models.
The results of this review highlight several challenges related to the use of wearable technology and DL methods in the assessment of PD, despite the advantages this technology could bring in the development and implementation of automated systems for PD assessment
Hand tracking for clinical applications: validation of the Google MediaPipe Hand (GMH) and the depth-enhanced GMH-D frameworks
Accurate 3D tracking of hand and fingers movements poses significant
challenges in computer vision. The potential applications span across multiple
domains, including human-computer interaction, virtual reality, industry, and
medicine. While gesture recognition has achieved remarkable accuracy,
quantifying fine movements remains a hurdle, particularly in clinical
applications where the assessment of hand dysfunctions and rehabilitation
training outcomes necessitate precise measurements. Several novel and
lightweight frameworks based on Deep Learning have emerged to address this
issue; however, their performance in accurately and reliably measuring fingers
movements requires validation against well-established gold standard systems.
In this paper, the aim is to validate the handtracking framework implemented by
Google MediaPipe Hand (GMH) and an innovative enhanced version, GMH-D, that
exploits the depth estimation of an RGB-Depth camera to achieve more accurate
tracking of 3D movements. Three dynamic exercises commonly administered by
clinicians to assess hand dysfunctions, namely Hand Opening-Closing, Single
Finger Tapping and Multiple Finger Tapping are considered. Results demonstrate
high temporal and spectral consistency of both frameworks with the gold
standard. However, the enhanced GMH-D framework exhibits superior accuracy in
spatial measurements compared to the baseline GMH, for both slow and fast
movements. Overall, our study contributes to the advancement of hand tracking
technology, the establishment of a validation procedure as a good-practice to
prove efficacy of deep-learning-based hand-tracking, and proves the
effectiveness of GMH-D as a reliable framework for assessing 3D hand movements
in clinical applications
Tauopathies with parkinsonism: clinical spectrum, neuropathologic basis, biological markers, and treatment options
Peer Reviewedhttp://deepblue.lib.umich.edu/bitstream/2027.42/73913/1/j.1468-1331.2008.02513.x.pd
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