Objective Assessment of Bradykinesia Estimated from the Wrist Extension in Older Adults and Patients with Parkinson’s Disease

Rabelo, Amanda Gomes; Neves, Lucio Pereira; Paixão, Ana Paula S.; Oliveira, Fábio Henrique Monteiro; Souza, Luciane Aparecida Pascucci Sande de; Vieira, Marcus Fraga; Pereira, Adriano Alves; Andrade, Adriano O.

doi:10.1007/s10439-017-1908-3

Cited by 34 publications

(51 citation statements)

References 39 publications

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“…This study compared the KNN, SVM, RF, and NB classifiers, KNN is currently used as a benchmark for this type of application, and the SVM, RF and NB classifiers are widely used today, which means that this comparison can aid the PD diagnosis in becoming more effective [ 28 – 30 ].…”

Section: Discussionmentioning

confidence: 99%

Discrimination between healthy and patients with Parkinson’s disease from hand resting activity using inertial measurement unit

Peres

Calil

Silva³

et al. 2021

BioMed Eng OnLine

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Background Parkinson’s disease (PD) is a neurological disease that affects the motor system. The associated motor symptoms are muscle rigidity or stiffness, bradykinesia, tremors, and gait disturbances. The correct diagnosis, especially in the initial stages, is fundamental to the life quality of the individual with PD. However, the methods used for diagnosis of PD are still based on subjective criteria. As a result, the objective of this study is the proposal of a method for the discrimination of individuals with PD (in the initial stages of the disease) from healthy groups, based on the inertial sensor recordings. Methods A total of 27 participants were selected, 15 individuals previously diagnosed with PD and 12 healthy individuals. The data collection was performed using inertial sensors (positioned on the back of the hand and on the back of the forearm). Different numbers of features were used to compare the values of sensitivity, specificity, precision, and accuracy of the classifiers. For group classification, 4 classifiers were used and compared, those being [Random Forest (RF), Support Vector Machine (SVM), K-Nearest Neighbor (KNN), and Naive Bayes (NB)]. Results When all individuals with PD were analyzed, the best performance for sensitivity and accuracy (0.875 and 0.800, respectively) was found in the SVM classifier, fed with 20% and 10% of the features, respectively, while the best performance for specificity and precision (0.933 and 0.917, respectively) was associated with the RF classifier fed with 20% of all the features. When only individuals with PD and score 1 on the Hoehn and Yahr scale (HY) were analyzed, the best performances for sensitivity, precision and accuracy (0.933, 0.778 and 0.848, respectively) were from the SVM classifier, fed with 40% of all features, and the best result for precision (0.800) was connected to the NB classifier, fed with 20% of all features. Conclusion Through an analysis of all individuals in this study with PD, the best classifier for the detection of PD (sensitivity) was the SVM fed with 20% of the features and the best classifier for ruling out PD (specificity) was the RF classifier fed with 20% of the features. When analyzing individuals with PD and score HY = 1, the SVM classifier was superior across the sensitivity, precision, and accuracy, and the NB classifier was superior in the specificity. The obtained result indicates that objective methods can be applied to help in the evaluation of PD.

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Section: Discussionmentioning

confidence: 99%

Discrimination between healthy and patients with Parkinson’s disease from hand resting activity using inertial measurement unit

Peres

Calil

Silva³

et al. 2021

BioMed Eng OnLine

Self Cite

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“…5 can support the development of a score to quantify the severity of some of cardinal signs. For example, Amanda et al [20] studied bradykinesia by quantifying the flexion and extension of the wrist. Pose versus Gravity was evaluated in [26] for the estimation of hand tremor parameters.…”

Section: Discussionmentioning

confidence: 99%

“…Signals collected from the gyroscope were sampled at 50 Hz (with sensitivity of ± 500 º/s) [20] and from the NCC system at 3 kHz. This sample rate was chosen because of the sensor behavior, in a way that, after carrier removal, 50 points are left for every second.…”

Section: Methodsmentioning

confidence: 99%

A non-contact system for the assessment of hand motor tasks in people with Parkinson’s disease

et al. 2021

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Clinical diagnosis of Parkinson’s disease (PD) motor symptoms remains a problem. Most of the current studies focus on objective evaluations to make the evaluation more reliable. Most of these systems are based on the use of inertial and electromyographic sensors that require contact with the body part being assessed. Contact sensors restrict natural movement, may be uncomfortable and may require preparation of the body, which may cause irritation. As an alternative to contact sensors for the study of hand motor tasks performed by subjects with and without PD, electrical potential sensing technology is used in this research. A custom hardware has been designed to enable data collection by hand movement. A micro-machine system validated the developed system, and a relationship model was established between hand displacement and non-contact capacitive (NCC) sensor response. An experiment was conducted, including 57 subjects, 30 with PD (experimental group) and 27 healthy control group, followed by an analysis of statistical features extracted from the instantaneous mean frequency (IMNF) of NCC sensor. These results were compared with those obtained from gyroscope signals that are considered in the field to be the gold standard. As a result, NCC responses were correlated linearly with hand displacement (R2 = 0.7692 and $${\text{R}}_{\text{adj}}^{2}$$ R adj 2 = 0.7631). The statistical evaluation of IMNF features showed, that both, contact and non-contact sensors, were able to discriminate movement patterns of the control group from the experimental one. The results confirm statistical similarity between features extracted from NCC and gyroscope signals.

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“…Although the recurrence of tremor is highly linked to PD, the extent of bradykinesia is much higher in advanced phases of the disease [6] compared to early stages. With respect to the performance of daily life activities and particularly, with upper limb function, the patient faces enormous difficulty because of tremor and bradykinesia, which certainly decrease the QoL of the PD patient.…”

Section: Introductionmentioning

confidence: 99%

A-WEAR Bracelet for Detection of Hand Tremor and Bradykinesia in Parkinson’s Patients

Channa

Ifrim

Popescu

et al. 2021

Sensors

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Parkinson’s disease patients face numerous motor symptoms that eventually make their life different from those of normal healthy controls. Out of these motor symptoms, tremor and bradykinesia, are relatively prevalent in all stages of this disease. The assessment of these symptoms is usually performed by traditional methods where the accuracy of results is still an open question. This research proposed a solution for an objective assessment of tremor and bradykinesia in subjects with PD (10 older adults aged greater than 60 years with tremor and 10 older adults aged greater than 60 years with bradykinesia) and 20 healthy older adults aged greater than 60 years. Physical movements were recorded by means of an AWEAR bracelet developed using inertial sensors, i.e., 3D accelerometer and gyroscope. Participants performed upper extremities motor activities as adopted by neurologists during the clinical assessment based on Unified Parkinson’s Disease Rating Scale (UPDRS). For discriminating the patients from healthy controls, temporal and spectral features were extracted, out of which non-linear temporal and spectral features show greater difference. Both supervised and unsupervised machine learning classifiers provide good results. Out of 40 individuals, neural net clustering discriminated 34 individuals in correct classes, while the KNN approach discriminated 91.7% accurately. In a clinical environment, the doctor can use the device to comprehend the tremor and bradykinesia of patients quickly and with higher accuracy.

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Objective Assessment of Bradykinesia Estimated from the Wrist Extension in Older Adults and Patients with Parkinson’s Disease

Cited by 34 publications

References 39 publications

Discrimination between healthy and patients with Parkinson’s disease from hand resting activity using inertial measurement unit

Discrimination between healthy and patients with Parkinson’s disease from hand resting activity using inertial measurement unit

A non-contact system for the assessment of hand motor tasks in people with Parkinson’s disease

A-WEAR Bracelet for Detection of Hand Tremor and Bradykinesia in Parkinson’s Patients

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