An upper-limbs activities analysis of PD patients in OFF and ON state of medication

Bravo, M.; Bermeo, A.; Contreras, Rafael; Huerta, Mónica; Soto, A.; Bermeo, J. P.; Sagbay, G.; Llumiguano, C.

doi:10.1109/etcm.2016.7750872

Cited by 6 publications

(15 citation statements)

References 14 publications

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“…The number of sensors ranged from one to 17. Thirty-seven studies used only one sensor, either on the finger, hand, wrist or forearm [15, 50-52, 59, 61, 63, 68, 69, 72, 75, 79, 84, 89, 91, 94, 96, 97, 99, 100, 104, 106, 108, 109, 112-114, 117, 118, 122-124, 126, 127, 131, 134, 140], while seven studies used two sensors bilaterally placed on the hand or wrist [60, 66, 78, 81, 82, 133, 137]. Nine studies used two sensors of which the majority placed one on the thumb and one the index finger [54, 58, 64, 70, 71, 73, 85, 88, 101], while Martinez-Manzara et al used one sensor on the hand and one on the finger [67], Samotus et al and Rahimi et al put one on the wrist and one on the index finger [95, 111] and Shawen et al put one on the hand and one on the wrist [86].…”

Section: Resultsmentioning

confidence: 99%

“…Spectral power was used in seven studies of which four reported sensitivity. Bravo et al compared power spectral density (PSD) plots between PD patients and healthy controls and found both higher and lower PSD amplitude for PD patients in comparison with healthy controls, depending on the individual [68]. In patients with dystonia, Legros et al found a decrease of the area under the spectrum curve after deep brain stimulation surgery [140].…”

Section: Rms Of Angularmentioning

confidence: 99%

“…Inertial measurement unit PD [72], stroke [117] 1/2 [66] † Dorsal hand Inertial measurement unit PD [66,68,75,84], ataxia [131], tremor [106, 109] 1/2 [78,81,82,133,137] † Wrist(s) Inertial measurement unit PD [78,81,82,100], stroke [15,118,122], CP [137], MS [123,124,126], ataxia [133], tremor [ ܶ ൌ ܶ ௧ െ ܶ [28]; Inter-tap-interval (ITI) [144]; mean, standard deviation, minimum, maximum, range, interquartile range, median, and tenth and ninetieth percentiles of time [133]; duration of sub movements [134] Stroke [15,28,117], PD [11,49,54,71,75,80], CP [12,137], tremor [102,144], MS [123,124], ataxia [132,134]…”

Section: Forearmmentioning

confidence: 99%

“…Eating was included in six studies [48,52,68,77,84,87] as well as pouring water [76,86,108,113,126,127] whereas reaching/grasping objects was included in five studies [117,121,126,127,137]. Teeth brushing was included in four studies [52,68,77,84] as well as putting clothes on/off [47,48,77,87]. In stroke, the Wolf Motor Function test or parts of this clinical scale were included four times [115,[118][119][120] and four studies measured activities in an unrestricted home environment [63,89,134,139].…”

Section: Upper Limb Tasksmentioning

confidence: 99%

“…; [62] Stroke [15], PD [62], ataxia [129] Amplitude and dominant frequency of modulation associated with acceleration PD [49] Average magnitude of 1 st five STFT components HD [29] Fractal dimension acceleration PD [49] Spectral power Frequency band 1.5-3Hz & 5-8Hz [118]; Frequency band dyskinesia: 0.3-3Hz [77]; Frequency band 0.5-15Hz & 1-4Hz [87]; Frequency band 0.6-16Hz [140]; Welch PSD of displacement; Power Spectral Density plots [68,84]; PSD ratio 0.5-4Hz & 4-12Hz [102]; logarithm of the peak in the power spectrum in the tremor frequency band [113] Stroke [118], PD [68,77,84,87], dystonia [140], tremor [102,113] Peak power Gyroscope; 0-4Hz frequency band [73]; Power of dominant frequency [87]; Position and amplitude of dominant peaks in power spectra of acceleration signals [104] PD [59,60,73,87,99], tremor [102,104,105,110], ataxia [134], MS [125] Second peak power Power of second dominant frequency [87] PD [87] Total power Power spectrum of angular velocity [59]; Gyroscope; 0-4Hz frequency band [73]; power ratio AC...…”

Section: Energy Accelerationmentioning

confidence: 99%

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Assessment of upper limb movement disorders using wearable sensors during functional tasks: a systematic review

Vanmechelen

Haberfehlner

Vleeschhauwer

et al. 2022

Preprint

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Background: Studies aiming to objectively quantify upper limb movement disorders during functional 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 select the most sensitive sensor features for symptom detection and quantification and discuss application of the proposed methods in clinical practice. 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 functional 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 Parkinsons Disease. Wrist(s), hand and index finger were the most popular sensor locations. The most frequent tasks for assessment were: finger tapping, wrist pro/supination, keeping the arms extended in front of the body and finger-to-nose. The most frequently calculated sensor features were mean, standard deviation, root-mean-square, ranges, skewness, kurtosis and entropy of acceleration and/or angular velocity, in combination with dominant frequencies and 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 to select the most sensitive pathology-dependent sensor features and measurement methodologies for detection and quantification of upper limb movement disorders and for the objective evaluations of treatment effects. The insights from Parkinsons 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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Section: Resultsmentioning

confidence: 99%

Section: Rms Of Angularmentioning

confidence: 99%

Section: Forearmmentioning

confidence: 99%

Section: Upper Limb Tasksmentioning

confidence: 99%

Section: Energy Accelerationmentioning

confidence: 99%

See 3 more Smart Citations

Assessment of upper limb movement disorders using wearable sensors during functional tasks: a systematic review

Vanmechelen

Haberfehlner

Vleeschhauwer

et al. 2022

Preprint

View full text Add to dashboard Cite

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Artificial Neural Network Applied like Qualifier of Symptoms in Patients with Parkinson’s Disease by Evaluating the Movement of Upper-Limbs Activities

et al. 2018

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A system for finger tremor quantification in patients with Parkinson's disease

Bravo

Bermeo

Huerta³

et al. 2017

2017 39th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC)

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The current diagnosis of Parkinson's disease (PD) is based on a subjective assessment by the specialist. The monitoring of the tremor that presents in the hand index fingers in a patient with Parkinson's is one of the most important parameters to diagnose the evolution of the disease in an objective manner. This research analyze the tremor in the hand index fingers of patients with PD with medication and without medication. A sensor based in a triaxial accelerometer was used to acquire the data produced by the acceleration changes of parkinsonian tremor in the case of three activities: postural tremor, action tremor and rest tremor. Acquired data were processed in Matlab; the data were filtered and the spectral power density (PSD) was estimated with the Burg periodogram. It has been verified that the system presented in this article can accurately detect the parkinsonian tremors of the patients evaluated, additionally has been found that with the medication the tremors do not disappear completely, these remained with the same frequencies of PD but with a very small amplitude.

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An upper-limbs activities analysis of PD patients in OFF and ON state of medication

Cited by 6 publications

References 14 publications

Assessment of upper limb movement disorders using wearable sensors during functional tasks: a systematic review

Assessment of upper limb movement disorders using wearable sensors during functional tasks: a systematic review

Artificial Neural Network Applied like Qualifier of Symptoms in Patients with Parkinson’s Disease by Evaluating the Movement of Upper-Limbs Activities

A system for finger tremor quantification in patients with Parkinson's disease

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