Low Cost Parkinson’s Disease Early Detection and Classification Based on Voice and Electromyography Signal

Putri, Farika Tono; Ariyanto, Mochammad; Caesarendra, Wahyu; Ismail, Rifky; Pambudi, Kharisma Agung; Pasmanasari, Elta Diah

doi:10.1007/978-3-319-89629-8_15

Cited by 6 publications

(6 citation statements)

References 9 publications

Supporting

Mentioning

Contrasting

Unclassified

Order By: Relevance

“…Based on measuring device used, the existing work on Parkinsonian gait detection can be divided into three categories: specialized hardware-based [9], sensor-based [10]–[13], and smartphone-based [14]–[16].…”

Section: Related Workmentioning

confidence: 99%

See 1 more Smart Citation

Non-Contact Early Warning of Shaking Palsy

Yang

Fan

Ren

et al. 2019

IEEE J. Transl. Eng. Health Med.

View full text Add to dashboard Cite

Parkinsonian gait is a defining feature of shaking palsy (SP) and it has one of the worse impacts on human healthy life than other SP symptoms. The objective of this work is to propose a Parkinsonian gait detection system based on an S-band perception technique to classify abnormal gait and normal walking. Due to the differences in the Gaits of Parkinson’s patients compared with healthy persons, the wireless signals reflect and generates different variations at the receiver that could be used for SP diagnosis and classification. To detect a Parkinsonian gait, we first implement data preprocessing of the original data to obtain clear amplitude and phase information. Then, the feature extraction is carried out by principal component analysis (PCA). Finally, a support vector machine (SVM) classification algorithm is applied on collected data to classify the abnormal gait of SP patients compared with a normal gait. We evaluate the proposed system with different people, and the experimental outcomes show that the Parkinsonian gait detection of this training-based system achieves a high accuracy of above 90%. Moreover, the early warning of SP is achieved in a non-contact manner.

show abstract

Section: Related Workmentioning

confidence: 99%

“…Several studies have been conducted using tiny motion sensors to monitor gait such as accelerometers and pedometers [10]–[13]. The Dyna Port move monitor detected gait and different postures of patients with SP using a single and small wireless tri-axial accelerometer [10].…”

Section: Related Workmentioning

confidence: 99%

Non-Contact Early Warning of Shaking Palsy

Yang

Fan

Ren

et al. 2019

IEEE J. Transl. Eng. Health Med.

View full text Add to dashboard Cite

show abstract

“…El número de reglas de un modelo difuso en función del número de entradas está dado por la ecuación (17).…”

Section: P N 6 16 =^Hunclassified

“…Otros propósitos del uso de señales de voz es la formulación e implementación de sistemas para la distinción del habla [7]- [9], reconocimiento del habla [10], reconocimiento de lenguaje [11], reconocimiento de emociones basados en habla y en género [12], [13], diagnóstico de enfermedades patológicas como la disfonía y la laringitis [14], diagnóstico de nódulos, edemas y parálisis unilateral de las cuerdas vocales [15], reconocimiento de disartria en sistemas de reconocimiento automático de habla [16] y la detección temprana de Parkinson mediante voz [17].…”

Section: Introductionunclassified

“…La aparición y evolución de enfoques de clasificación que utilizan modelos de lógica difusa, redes neuronales artificiales, redes adaptativas de inferencia neurodifusa o aprendizaje automático en sistemas de control ha generado numerosos estudios e implementaciones en sistemas de detección, reconocimiento o clasificación. Se han propuesto modelos de redes neuronales [10], [13], [17], algoritmos genéticos [2], sistemas de inferencia neurodifusa (ANFIS) [3], y máquinas de soporte vectorial (SVM) [6] con el fin de clasificar con un margen de error bajo el género de una señal de voz humana. En este artículo, la clasificación se realiza mediante la aplicación de algoritmos de optimización a modelos difusos.…”

Section: Introductionunclassified

See 1 more Smart Citation

Gender classification based on voice signals using fuzzy models and optimization algorithms

Martinez

Espitia

2019

ITECKNE

View full text Add to dashboard Cite

This paper describes a gender classification scheme based on voice signals in which 16 different fuzzy models are proposed and optimized using four bio-inspired optimization algorithms and the quasi-Newton method. The classification scheme considers four data sets and five different voice features to define the input values of an algorithm in the optimization process. The inputs of each fuzzy model define the mean and variance of their Gaussian membership functions, and their fitness is evaluated by the input values of the algorithm and mean squared error as objective function to be minimized. A comparative analysis between models, algorithms and data sets is made to obtain conclusions according to the results of each optimized model.

show abstract