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Smid A, Dominguez-Vega ZT, van Laar T, Oterdoom DLM, Absalom AR, van Egmond ME, Drost G, van Dijk JMC. Objective clinical registration of tremor, bradykinesia, and rigidity during awake stereotactic neurosurgery: a scoping review. Neurosurg Rev 2024; 47:81. [PMID: 38355824 PMCID: PMC10866747 DOI: 10.1007/s10143-024-02312-4] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/06/2023] [Revised: 01/19/2024] [Accepted: 01/28/2024] [Indexed: 02/16/2024]
Abstract
Tremor, bradykinesia, and rigidity are incapacitating motor symptoms that can be suppressed with stereotactic neurosurgical treatment like deep brain stimulation (DBS) and ablative surgery (e.g., thalamotomy, pallidotomy). Traditionally, clinicians rely on clinical rating scales for intraoperative evaluation of these motor symptoms during awake stereotactic neurosurgery. However, these clinical scales have a relatively high inter-rater variability and rely on experienced raters. Therefore, objective registration (e.g., using movement sensors) is a reasonable extension for intraoperative assessment of tremor, bradykinesia, and rigidity. The main goal of this scoping review is to provide an overview of electronic motor measurements during awake stereotactic neurosurgery. The protocol was based on the PRISMA extension for scoping reviews. After a systematic database search (PubMed, Embase, and Web of Science), articles were screened for relevance. Hundred-and-three articles were subject to detailed screening. Key clinical and technical information was extracted. The inclusion criteria encompassed use of electronic motor measurements during stereotactic neurosurgery performed under local anesthesia. Twenty-three articles were included. These studies had various objectives, including correlating sensor-based outcome measures to clinical scores, identifying optimal DBS electrode positions, and translating clinical assessments to objective assessments. The studies were highly heterogeneous in device choice, sensor location, measurement protocol, design, outcome measures, and data analysis. This review shows that intraoperative quantification of motor symptoms is still limited by variable signal analysis techniques and lacking standardized measurement protocols. However, electronic motor measurements can complement visual evaluations and provide objective confirmation of correct placement of the DBS electrode and/or lesioning. On the long term, this might benefit patient outcomes and provide reliable outcome measures in scientific research.
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Affiliation(s)
- Annemarie Smid
- Department of Neurosurgery, University Medical Center Groningen, University of Groningen, Hanzeplein 1 HPC AB71, 9713 GZ, Groningen, Netherlands.
| | - Zeus T Dominguez-Vega
- Department of Neurology, University Medical Center Groningen, University of Groningen, Hanzeplein 1 HPC AB71, 9713 GZ, Groningen, Netherlands
| | - Teus van Laar
- Department of Neurology, University Medical Center Groningen, University of Groningen, Hanzeplein 1 HPC AB71, 9713 GZ, Groningen, Netherlands
| | - D L Marinus Oterdoom
- Department of Neurosurgery, University Medical Center Groningen, University of Groningen, Hanzeplein 1 HPC AB71, 9713 GZ, Groningen, Netherlands
| | - Anthony R Absalom
- Department of Anesthesiology, University Medical Center Groningen, University of Groningen, Hanzeplein 1 HPC AB71, 9713 GZ, Groningen, Netherlands
| | - Martje E van Egmond
- Department of Neurology, University Medical Center Groningen, University of Groningen, Hanzeplein 1 HPC AB71, 9713 GZ, Groningen, Netherlands
| | - Gea Drost
- Department of Neurosurgery, University Medical Center Groningen, University of Groningen, Hanzeplein 1 HPC AB71, 9713 GZ, Groningen, Netherlands
- Department of Neurology, University Medical Center Groningen, University of Groningen, Hanzeplein 1 HPC AB71, 9713 GZ, Groningen, Netherlands
| | - J Marc C van Dijk
- Department of Neurosurgery, University Medical Center Groningen, University of Groningen, Hanzeplein 1 HPC AB71, 9713 GZ, Groningen, Netherlands
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Vanmechelen I, Haberfehlner H, De Vleeschhauwer J, Van Wonterghem E, Feys H, Desloovere K, Aerts JM, Monbaliu E. Assessment of movement disorders using wearable sensors during upper limb tasks: A scoping review. Front Robot AI 2023; 9:1068413. [PMID: 36714804 PMCID: PMC9879015 DOI: 10.3389/frobt.2022.1068413] [Citation(s) in RCA: 3] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/12/2022] [Accepted: 11/30/2022] [Indexed: 01/10/2023] Open
Abstract
Background: Studies aiming to objectively quantify movement disorders during upper limb tasks using wearable sensors have recently increased, but there is a wide variety in described measurement and analyzing methods, hampering standardization of methods in research and clinics. Therefore, the primary objective of this review was to provide an overview of sensor set-up and type, included tasks, sensor features and methods used to quantify movement disorders during upper limb tasks in multiple pathological populations. The secondary objective was to identify the most sensitive sensor features for the detection and quantification of movement disorders on the one hand and to describe the clinical application of the proposed methods on the other hand. Methods: A literature search using Scopus, Web of Science, and PubMed was performed. Articles needed to meet following criteria: 1) participants were adults/children with a neurological disease, 2) (at least) one sensor was placed on the upper limb for evaluation of movement disorders during upper limb tasks, 3) comparisons between: groups with/without movement disorders, sensor features before/after intervention, or sensor features with a clinical scale for assessment of the movement disorder. 4) Outcome measures included sensor features from acceleration/angular velocity signals. Results: A total of 101 articles were included, of which 56 researched Parkinson's Disease. Wrist(s), hand(s) and index finger(s) were the most popular sensor locations. Most frequent tasks were: finger tapping, wrist pro/supination, keeping the arms extended in front of the body and finger-to-nose. Most frequently calculated sensor features were mean, standard deviation, root-mean-square, ranges, skewness, kurtosis/entropy of acceleration and/or angular velocity, in combination with dominant frequencies/power of acceleration signals. Examples of clinical applications were automatization of a clinical scale or discrimination between a patient/control group or different patient groups. Conclusion: Current overview can support clinicians and researchers in selecting the most sensitive pathology-dependent sensor features and methodologies for detection and quantification of upper limb movement disorders and objective evaluations of treatment effects. Insights from Parkinson's Disease studies can accelerate the development of wearable sensors protocols in the remaining pathologies, provided that there is sufficient attention for the standardisation of protocols, tasks, feasibility and data analysis methods.
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Affiliation(s)
- Inti Vanmechelen
- Research Group for Neurorehabilitation (eNRGy), KU Leuven Bruges, Department of Rehabilitation Sciences, Bruges, Belgium,*Correspondence: Inti Vanmechelen,
| | - Helga Haberfehlner
- Research Group for Neurorehabilitation (eNRGy), KU Leuven Bruges, Department of Rehabilitation Sciences, Bruges, Belgium,Amsterdam Movement Sciences, Amsterdam UMC, Department of Rehabilitation Medicine, Amsterdam, Netherlands
| | - Joni De Vleeschhauwer
- Research Group for Neurorehabilitation (eNRGy), KU Leuven, Department of Rehabilitation Sciences, Leuven, Belgium
| | - Ellen Van Wonterghem
- Research Group for Neurorehabilitation (eNRGy), KU Leuven Bruges, Department of Rehabilitation Sciences, Bruges, Belgium
| | - Hilde Feys
- Research Group for Neurorehabilitation (eNRGy), KU Leuven, Department of Rehabilitation Sciences, Leuven, Belgium
| | - Kaat Desloovere
- Research Group for Neurorehabilitation (eNRGy), KU Leuven, Department of Rehabilitation Sciences, Pellenberg, Belgium
| | - Jean-Marie Aerts
- Division of Animal and Human Health Engineering, KU Leuven, Department of Biosystems, Measure, Model and Manage Bioresponses (M3-BIORES), Leuven, Belgium
| | - Elegast Monbaliu
- Research Group for Neurorehabilitation (eNRGy), KU Leuven Bruges, Department of Rehabilitation Sciences, Bruges, Belgium
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Rabelo A, Folador JP, Cabral AM, Lima V, Arantes AP, Sande L, Vieira MF, de Almeida RMA, Andrade ADO. Identification and Characterization of Short-Term Motor Patterns in Rest Tremor of Individuals with Parkinson's Disease. Healthcare (Basel) 2022; 10:healthcare10122536. [PMID: 36554060 PMCID: PMC9778910 DOI: 10.3390/healthcare10122536] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/26/2022] [Revised: 11/25/2022] [Accepted: 12/06/2022] [Indexed: 12/23/2022] Open
Abstract
(1) Background: The dynamics of hand tremors involve nonrandom and short-term motor patterns (STMPs). This study aimed to (i) identify STMPs in Parkinson’s disease (PD) and physiological resting tremor and (ii) characterize STMPs by amplitude, persistence, and regularity. (2) Methods: This study included healthy (N = 12, 60.1 ± 5.9 years old) and PD (N = 14, 65 ± 11.54 years old) participants. The signals were collected using a triaxial gyroscope on the dorsal side of the hand during a resting condition. Data were preprocessed and seven features were extracted from each 1 s window with 50% overlap. The STMPs were identified using the clustering technique k-means applied to the data in the two-dimensional space given by t-Distributed Stochastic Neighbor Embedding (t-SNE). The frequency, transition probability, and duration of the STMPs for each group were assessed. All STMP features were averaged across groups. (3) Results: Three STMPs were identified in tremor signals (p < 0.05). STMP 1 was prevalent in the healthy control (HC) subjects, STMP 2 in both groups, and STMP3 in PD. Only the coefficient of variation and complexity differed significantly between groups. (4) Conclusion: These results can help professionals characterize and evaluate tremor severity and treatment efficacy.
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Affiliation(s)
- Amanda Rabelo
- Centre for Innovation and Technology Assessment in Health (NIATS), Faculty of Electrical Engineering, Federal University of Uberlândia, Uberlândia 38400-902, Brazil
- Correspondence: ; Tel.: +55-34-99812-3330
| | - João Paulo Folador
- Centre for Innovation and Technology Assessment in Health (NIATS), Faculty of Electrical Engineering, Federal University of Uberlândia, Uberlândia 38400-902, Brazil
| | - Ariana Moura Cabral
- Centre for Innovation and Technology Assessment in Health (NIATS), Faculty of Electrical Engineering, Federal University of Uberlândia, Uberlândia 38400-902, Brazil
| | - Viviane Lima
- Centre for Innovation and Technology Assessment in Health (NIATS), Faculty of Electrical Engineering, Federal University of Uberlândia, Uberlândia 38400-902, Brazil
| | - Ana Paula Arantes
- Neuroscience Department, Hotchkiss Brain Institute, University of Calgary, Calgary, AB T2N 1N4, Canada
| | - Luciane Sande
- Neuroscience and Motor Control Labotaroty (Neurocom), Federal University of Triagulo Mineiro (UFTM), Uberaba 38025-350, Brazil
| | - Marcus Fraga Vieira
- Bioengineering and Biomechanics Laboratory, Federal University of Goiás, Goiânia 74690-900, Brazil
| | | | - Adriano de Oliveira Andrade
- Centre for Innovation and Technology Assessment in Health (NIATS), Faculty of Electrical Engineering, Federal University of Uberlândia, Uberlândia 38400-902, Brazil
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Ma C, Zhang P, Wang J, Zhang J, Pan L, Li X, Yin C, Li A, Zong R, Zhang Z. Objective quantification of the severity of postural tremor based on kinematic parameters: A multi-sensory fusion study. COMPUTER METHODS AND PROGRAMS IN BIOMEDICINE 2022; 219:106741. [PMID: 35338882 DOI: 10.1016/j.cmpb.2022.106741] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 09/27/2021] [Revised: 02/27/2022] [Accepted: 03/08/2022] [Indexed: 06/14/2023]
Abstract
BACKGROUND Current clinical assessments of essential tremor (ET) are primarily based on expert consultation combined with reviewing patient complaints, physician expertise, and diagnostic experience. Thus, traditional evaluation methods often lead to biased diagnostic results. There is a clinical demand for a method that can objectively quantify the severity of the patient's disease. METHODS This study aims to develop an artificial intelligence-aided diagnosis method based on multi-sensory fusion wearables. The experiment relies on a rigorous clinical trial paradigm to collect multi-modal fusion of signals from 98 ET patients. At the same time, three clinicians scored independently, and the consensus score obtained was used as the ground truth for the machine learning models. RESULTS Sixty kinematic parameters were extracted from the signals recorded by the nine-axis inertial measurement unit (IMU). The results showed that most of the features obtained by IMU could effectively characterize the severity of the tremors. The accuracy of the optimal model for three tasks classifying five severity levels was 97.71%, 97.54%, and 97.72%, respectively. CONCLUSIONS This paper reports the first attempt to combine multiple feature selection and machine learning algorithms for fine-grained automatic quantification of postural tremor in ET patients. The promising results showed the potential of the proposed approach to quantify the severity of ET objectively.
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Affiliation(s)
- Chenbin Ma
- Center for Artificial Intelligence in Medicine, Medical Innovation Research Department, PLA General Hospital, 100853, Beijing, China; Beijing Advanced Innovation Center for Biomedical Engineering, Beihang University, 100191, Beijing, China; School of Biological Science and Medical Engineering, Beihang University, 100191, Beijing, China; Shenyuan Honors College, Beihang University, 100191, Beijing, China
| | - Peng Zhang
- Beijing Advanced Innovation Center for Biomedical Engineering, Beihang University, 100191, Beijing, China; School of Biological Science and Medical Engineering, Beihang University, 100191, Beijing, China
| | - Jiachen Wang
- Medical School of Chinese PLA, 100853, Beijing, China
| | - Jian Zhang
- Medical School of Chinese PLA, 100853, Beijing, China
| | - Longsheng Pan
- Department of Neurosurgery, First Medical Center of Chinese PLA General Hospital, 100853, Beijing, China
| | - Xuemei Li
- Clinics of Cadre, Department of Outpatient, First Medical Center of Chinese PLA General Hospital, 100853, Beijing, China
| | - Chunyu Yin
- Clinics of Cadre, Department of Outpatient, First Medical Center of Chinese PLA General Hospital, 100853, Beijing, China
| | - Ailing Li
- Pusheng Yixin (Beijing) Hospital Management Co., Ltd, 100020, Beijing, China
| | - Rui Zong
- Department of Neurosurgery, First Medical Center of Chinese PLA General Hospital, 100853, Beijing, China.
| | - Zhengbo Zhang
- Center for Artificial Intelligence in Medicine, Medical Innovation Research Department, PLA General Hospital, 100853, Beijing, China.
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Smid A, Elting JWJ, van Dijk JMC, Otten B, Oterdoom DLM, Tamasi K, Heida T, van Laar T, Drost G. Intraoperative Quantification of MDS-UPDRS Tremor Measurements Using 3D Accelerometry: A Pilot Study. J Clin Med 2022; 11:jcm11092275. [PMID: 35566401 PMCID: PMC9104023 DOI: 10.3390/jcm11092275] [Citation(s) in RCA: 7] [Impact Index Per Article: 3.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/18/2022] [Revised: 04/10/2022] [Accepted: 04/16/2022] [Indexed: 02/05/2023] Open
Abstract
The most frequently used method for evaluating tremor in Parkinson’s disease (PD) is currently the internationally standardized Movement Disorder Society—Unified PD Rating Scale (MDS-UPDRS). However, the MDS-UPDRS is associated with limitations, such as its inherent subjectivity and reliance on experienced raters. Objective motor measurements using accelerometry may overcome the shortcomings of visually scored scales. Therefore, the current study focuses on translating the MDS-UPDRS tremor tests into an objective scoring method using 3D accelerometry. An algorithm to measure and classify tremor according to MDS-UPDRS criteria is proposed. For this study, 28 PD patients undergoing neurosurgical treatment and 26 healthy control subjects were included. Both groups underwent MDS-UPDRS tests to rate tremor severity, while accelerometric measurements were performed at the index fingers. All measurements were performed in an off-medication state. Quantitative measures were calculated from the 3D acceleration data, such as tremor amplitude and area-under-the-curve of power in the 4−6 Hz range. Agreement between MDS-UPDRS tremor scores and objective accelerometric scores was investigated. The trends were consistent with the logarithmic relationship between tremor amplitude and MDS-UPDRS score reported in previous studies. The accelerometric scores showed a substantial concordance (>69.6%) with the MDS-UPDRS ratings. However, accelerometric kinetic tremor measures poorly associated with the given MDS-UPDRS scores (R2 < 0.3), mainly due to the noise between 4 and 6 Hz found in the healthy controls. This study shows that MDS-UDPRS tremor tests can be translated to objective accelerometric measurements. However, discrepancies were found between accelerometric kinetic tremor measures and MDS-UDPRS ratings. This technology has the potential to reduce rater dependency of MDS-UPDRS measurements and allow more objective intraoperative monitoring of tremor.
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Affiliation(s)
- Annemarie Smid
- Department of Neurosurgery, University Medical Center Groningen, University of Groningen, Hanzeplein 1, 9713 GZ Groningen, The Netherlands; (J.M.C.v.D.); (D.L.M.O.); (K.T.); (G.D.)
- Correspondence:
| | - Jan Willem J. Elting
- Department of Neurology, University Medical Center Groningen, University of Groningen, Hanzeplein 1, 9713 GZ Groningen, The Netherlands; (J.W.J.E.); (T.v.L.)
- Expertise Center Movement Disorders Groningen, University Medical Center Groningen, Hanzeplein 1, 9713 GZ Groningen, The Netherlands
| | - J. Marc C. van Dijk
- Department of Neurosurgery, University Medical Center Groningen, University of Groningen, Hanzeplein 1, 9713 GZ Groningen, The Netherlands; (J.M.C.v.D.); (D.L.M.O.); (K.T.); (G.D.)
| | - Bert Otten
- Center for Human Movement Sciences, University Medical Center Groningen, University of Groningen, Hanzeplein 1, 9713 GZ Groningen, The Netherlands;
| | - D. L. Marinus Oterdoom
- Department of Neurosurgery, University Medical Center Groningen, University of Groningen, Hanzeplein 1, 9713 GZ Groningen, The Netherlands; (J.M.C.v.D.); (D.L.M.O.); (K.T.); (G.D.)
| | - Katalin Tamasi
- Department of Neurosurgery, University Medical Center Groningen, University of Groningen, Hanzeplein 1, 9713 GZ Groningen, The Netherlands; (J.M.C.v.D.); (D.L.M.O.); (K.T.); (G.D.)
- Department of Epidemiology, University Medical Center Groningen, University of Groningen, Hanzeplein 1, 9713 GZ Groningen, The Netherlands
| | - Tjitske Heida
- Department of Biomedical Signals and Systems, Faculty EEMCS, TechMed Centre, University of Twente, Drienerlolaan 5, 7522 NB Enschede, The Netherlands;
| | - Teus van Laar
- Department of Neurology, University Medical Center Groningen, University of Groningen, Hanzeplein 1, 9713 GZ Groningen, The Netherlands; (J.W.J.E.); (T.v.L.)
- Expertise Center Movement Disorders Groningen, University Medical Center Groningen, Hanzeplein 1, 9713 GZ Groningen, The Netherlands
| | - Gea Drost
- Department of Neurosurgery, University Medical Center Groningen, University of Groningen, Hanzeplein 1, 9713 GZ Groningen, The Netherlands; (J.M.C.v.D.); (D.L.M.O.); (K.T.); (G.D.)
- Department of Neurology, University Medical Center Groningen, University of Groningen, Hanzeplein 1, 9713 GZ Groningen, The Netherlands; (J.W.J.E.); (T.v.L.)
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Wearable sensors during drawing tasks to measure the severity of essential tremor. Sci Rep 2022; 12:5242. [PMID: 35347169 PMCID: PMC8960784 DOI: 10.1038/s41598-022-08922-6] [Citation(s) in RCA: 8] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/07/2021] [Accepted: 02/24/2022] [Indexed: 11/08/2022] Open
Abstract
Commonly used methods to assess the severity of essential tremor (ET) are based on clinical observation and lack objectivity. This study proposes the use of wearable accelerometer sensors for the quantitative assessment of ET. Acceleration data was recorded by inertial measurement unit (IMU) sensors during sketching of Archimedes spirals in 17 ET participants and 18 healthy controls. IMUs were placed at three points (dorsum of hand, posterior forearm, posterior upper arm) of each participant's dominant arm. Movement disorder neurologists who were blinded to clinical information scored ET patients on the Fahn-Tolosa-Marin rating scale (FTM) and conducted phenotyping according to the recent Consensus Statement on the Classification of Tremors. The ratio of power spectral density of acceleration data in 4-12 Hz to 0.5-4 Hz bands and the total duration of the action were inputs to a support vector machine that was trained to classify the ET subtype. Regression analysis was performed to determine the relationship of acceleration and temporal data with the FTM scores. The results show that the sensor located on the forearm had the best classification and regression results, with accuracy of 85.71% for binary classification of ET versus control. There was a moderate to good correlation (r2 = 0.561) between FTM and a combination of power spectral density ratio and task time. However, the system could not accurately differentiate ET phenotypes according to the Consensus classification scheme. Potential applications of machine-based assessment of ET using wearable sensors include clinical trials and remote monitoring of patients.
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Legaria-Santiago VK, Sánchez-Fernández LP, Sánchez-Pérez LA, Garza-Rodríguez A. Computer models evaluating hand tremors in Parkinson's disease patients. Comput Biol Med 2022; 140:105059. [PMID: 34847385 DOI: 10.1016/j.compbiomed.2021.105059] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/02/2021] [Revised: 11/12/2021] [Accepted: 11/20/2021] [Indexed: 11/03/2022]
Abstract
One of the most characteristic signs of Parkinson's disease (PD) is hand tremor. The MDS-UPDRS scale evaluates different aspects of the disease. The tremor score is a part of the MDS-UPDRS scale, which provides instructions for rating it, by observation, with an integer from 0 to 4. Nevertheless, this form of assessment is subjective and dependent on visual acuity, clinical judgment, and even the mood of the individual examiner. On the other hand, in many cases, existing computational models proposed to resolve the disadvantages of the MDS-UPDRS scale may have uncertainty in differentiating a category of a slight Parkinson tremor from voluntary movements. In this study, 554 measurements from Parkinson's patients, and 60 measurements from healthy subjects, were recorded with inertial sensors placed on the back of each hand. Five biomechanical indicators characterised the hand tremor. With these indicators, the three fuzzy inference models proposed can differentiate, in the first instance, the presence of postural or resting tremor from a normal movement of the hand, and if detected, to determine its severity. The fuzzy inference models allowed following the criteria of the MDS-UPDRS scale, providing an evaluation with an accuracy of two decimal digits and which, due to its simplicity, can be implemented in clinical environments. The assessments of three experts validated the computer model.
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Affiliation(s)
| | - Luis Pastor Sánchez-Fernández
- Instituto Politécnico Nacional, Centro de Investigación en Computación, Juan de Dios Bátiz, 07738 México City, Mexico.
| | - Luis Alejandro Sánchez-Pérez
- Electrical and Computer Engineering Department, University of Michigan, 4901 Evergreen Rd, Dearborn, MI 48128, USA
| | - Alejandro Garza-Rodríguez
- Instituto Politécnico Nacional, Centro de Investigación en Computación, Juan de Dios Bátiz, 07738 México City, Mexico
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Ingram LA, Carroll VK, Butler AA, Brodie MA, Gandevia SC, Lord SR. Quantifying upper limb motor impairment in people with Parkinson's disease: a physiological profiling approach. PeerJ 2021; 9:e10735. [PMID: 33604177 PMCID: PMC7869669 DOI: 10.7717/peerj.10735] [Citation(s) in RCA: 9] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/14/2020] [Accepted: 12/17/2020] [Indexed: 11/20/2022] Open
Abstract
BACKGROUND Upper limb motor impairments, such as slowness of movement and difficulties executing sequential tasks, are common in people with Parkinson's disease (PD). OBJECTIVE To evaluate the validity of the upper limb Physiological Profile Assessment (PPA) as a standard clinical assessment battery in people with PD, by determining whether the tests, which encompass muscle strength, dexterity, arm stability, position sense, skin sensation and bimanual coordination can (a) distinguish people with PD from healthy controls, (b) detect differences in upper limb test domains between "off" and "on" anti-Parkinson medication states and (c) correlate with a validated measure of upper limb function. METHODS Thirty-four participants with PD and 68 healthy controls completed the upper limb PPA tests within a single session. RESULTS People with PD exhibited impaired performance across most test domains. Based on validity, reliability and feasibility, six tests (handgrip strength, finger-press reaction time, 9-hole peg test, bimanual pole test, arm stability, and shirt buttoning) were identified as key tests for the assessment of upper limb function in people with PD. CONCLUSIONS The upper limb PPA provides a valid, quick and simple means of quantifying specific upper limb impairments in people with PD. These findings indicate clinical assessments should prioritise tests of muscle strength, unilateral movement and dexterity, bimanual coordination, arm stability and functional tasks in people with PD as these domains are the most commonly and significantly impaired.
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Affiliation(s)
- Lewis A. Ingram
- Neuroscience Research Australia, Sydney, New South Wales, Australia
- University of New South Wales, Sydney, New South Wales, Australia
| | - Vincent K. Carroll
- NSW Health, Mid North Coast Local Health District, Coffs Harbour, New South Wales, Australia
- Parkinson’s NSW, Sydney, New South Wales, Australia
| | - Annie A. Butler
- Neuroscience Research Australia, Sydney, New South Wales, Australia
- University of New South Wales, Sydney, New South Wales, Australia
| | - Matthew A. Brodie
- Neuroscience Research Australia, Sydney, New South Wales, Australia
- University of New South Wales, Sydney, New South Wales, Australia
| | - Simon C. Gandevia
- Neuroscience Research Australia, Sydney, New South Wales, Australia
- University of New South Wales, Sydney, New South Wales, Australia
| | - Stephen R. Lord
- Neuroscience Research Australia, Sydney, New South Wales, Australia
- University of New South Wales, Sydney, New South Wales, Australia
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Channa A, Popescu N, Ciobanu V. Wearable Solutions for Patients with Parkinson's Disease and Neurocognitive Disorder: A Systematic Review. SENSORS 2020; 20:s20092713. [PMID: 32397516 PMCID: PMC7249148 DOI: 10.3390/s20092713] [Citation(s) in RCA: 42] [Impact Index Per Article: 10.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 04/09/2020] [Revised: 05/05/2020] [Accepted: 05/06/2020] [Indexed: 01/01/2023]
Abstract
Prevalence of neurocognitive diseases in adult patients demands the use of wearable devices to transform the future of mental health. Recent development in wearable technology proclaimed its use in diagnosis, rehabilitation, assessment, and monitoring. This systematic review presents the state of the art of wearables used by Parkinson’s disease (PD) patients or the patients who are going through a neurocognitive disorder. This article is based on PRISMA guidelines, and the literature is searched between January 2009 to January 2020 analyzing four databases: PubMed, IEEE Xplorer, Elsevier, and ISI Web of Science. For further validity of articles, a new PEDro-inspired technique is implemented. In PEDro, five statistical indicators were set to classify relevant articles and later the citations were also considered to make strong assessment of relevant articles. This led to 46 articles that met inclusion criteria. Based on them, this systematic review examines different types of wearable devices, essential in improving early diagnose and monitoring, emphasizing their role in improving the quality of life, differentiating the various fitness and gait wearable-based exercises and their impact on the regression of disease and on the motor diagnosis tests and finally addressing the available wearable insoles and their role in rehabilitation. The research findings proved that sensor based wearable devices, and specially instrumented insoles, help not only in monitoring and diagnosis but also in tracking numerous exercises and their positive impact towards the improvement of quality of life among different Parkinson and neurocognitive patients.
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Gakopoulos S, Nica IG, Bekteshi S, Aerts JM, Monbaliu E, Hallez H. Development of a Data Logger for Capturing Human-Machine Interaction in Wheelchair Head-Foot Steering Sensor System in Dyskinetic Cerebral Palsy. SENSORS 2019; 19:s19245404. [PMID: 31817941 PMCID: PMC6960520 DOI: 10.3390/s19245404] [Citation(s) in RCA: 6] [Impact Index Per Article: 1.2] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 10/29/2019] [Revised: 12/01/2019] [Accepted: 12/05/2019] [Indexed: 11/16/2022]
Abstract
The use of data logging systems for capturing wheelchair and user behavior has increased rapidly over the past few years. Wheelchairs ensure more independent mobility and better quality of life for people with motor disabilities. Especially, for people with complex movement disorders, such as dyskinetic cerebral palsy (DCP) who lack the ability to walk or to handle objects, wheelchairs offer a means of integration into daily life. The mobility of DCP patients is based on a head-foot wheelchair steering system. In this work, a data logging system is proposed to capture data from human-wheelchair interaction for the head-foot steering system. Additionally, the data logger provides an interface to multiple Inertial Measurement Units (IMUs) placed on the body of the wheelchair user. The system provides accurate and real-time information from head-foot navigation system pressure sensors on the wheelchair during driving. This system was used as a tool to obtain further insights into wheelchair control and steering behavior of people diagnosed with DCP in comparison with a healthy subject.
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Affiliation(s)
- Sotirios Gakopoulos
- KU Leuven, Bruges Campus, Department of Computer Science, Mechatronics Research Group, Spoorwegstraat 12, 8200 Bruges, Belgium;
- Correspondence:
| | - Ioana Gabriela Nica
- KU Leuven, Department of Biosystems, Division of Animal and Human Health Engineering, Measure, Model and Manage Bioresponse (M3-BIORES), Kasteelpark Arenberg 30, 3001 Leuven, Belgium; (I.G.N.); (J.-M.A.)
| | - Saranda Bekteshi
- KU Leuven, Bruges Campus, Department of Rehabilitation Sciences, Research Group for Neurorehabilitation, Spoorwegstraat 12, 8200 Bruges, Belgium; (S.B.); (E.M.)
| | - Jean-Marie Aerts
- KU Leuven, Department of Biosystems, Division of Animal and Human Health Engineering, Measure, Model and Manage Bioresponse (M3-BIORES), Kasteelpark Arenberg 30, 3001 Leuven, Belgium; (I.G.N.); (J.-M.A.)
| | - Elegast Monbaliu
- KU Leuven, Bruges Campus, Department of Rehabilitation Sciences, Research Group for Neurorehabilitation, Spoorwegstraat 12, 8200 Bruges, Belgium; (S.B.); (E.M.)
| | - Hans Hallez
- KU Leuven, Bruges Campus, Department of Computer Science, Mechatronics Research Group, Spoorwegstraat 12, 8200 Bruges, Belgium;
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