Mobile Devices For The Real Time Detection Of Specific Human Motion Disorders

Lorenzi, Paolo; Rao, Rosario; Romano, Giulio; Kita, Ardian; Irrera, Fernanda

doi:10.1109/jsen.2016.2530944

Cited by 28 publications

(17 citation statements)

References 34 publications

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“…Table 6 shows the most recent papers about it. Most of them combine Video Recording and Acceleration [51,52,53,54,55,56]; Acceleration alone was used by [57,58,59]; Acceleration in combination with angular velocity by [60] and in combination with Inertial Measurement Unit sensor by [61,62]; Video Recording alone by [63]; Using Microelectromechanical systems by [64] and using Electroencephalography by [14,65]. The main objective of our research is to find an efficient system to detect and to stimulate with an affordable cost based on motor frequency analysis that can be improved with the implementation of neural networks and hip acceleration measures, in addition to exploring vibratory stimulation as a blockage of FOG.…”

Section: Resultsmentioning

confidence: 99%

A Non-Invasive Medical Device for Parkinson’s Patients with Episodes of Freezing of Gait

Punin

Barzallo

Clotet

et al. 2019

Sensors

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A critical symptom of Parkinson’s disease (PD) is the occurrence of Freezing of Gait (FOG), an episodic disorder that causes frequent falls and consequential injuries in PD patients. There are various auditory, visual, tactile, and other types of stimulation interventions that can be used to induce PD patients to escape FOG episodes. In this article, we describe a low cost wearable system for non-invasive gait monitoring and external delivery of superficial vibratory stimulation to the lower extremities triggered by FOG episodes. The intended purpose is to reduce the duration of the FOG episode, thus allowing prompt resumption of gait to prevent major injuries. The system, based on an Android mobile application, uses a tri-axial accelerometer device for gait data acquisition. Gathered data is processed via a discrete wavelet transform-based algorithm that precisely detects FOG episodes in real time. Detection activates external vibratory stimulation of the legs to reduce FOG time. The integration of detection and stimulation in one low cost device is the chief novel contribution of this work. We present analyses of sensitivity, specificity and effectiveness of the proposed system to validate its usefulness.

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Section: Resultsmentioning

confidence: 99%

A Non-Invasive Medical Device for Parkinson’s Patients with Episodes of Freezing of Gait

Punin

Barzallo

Clotet

et al. 2019

Sensors

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“…The authors of [16] used the accelerometer embedded on a mobile device to develop a method for the recognition of the resting and walking states, which implements an Artificial Neural Network (ANN) that receives as input the raw data acquired from the accelerometer, reporting an accuracy around 95%.…”

Section: Related Workmentioning

confidence: 99%

Pattern recognition techniques for the identification of Activities of Daily Living using mobile device accelerometer

Pires¹,

García²,

Pombo³

et al. 2019

Preprint

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This paper focuses on the recognition of Activities of Daily Living (ADL) applying pattern recognition techniques to the data acquired by the accelerometer available in the mobile devices. The recognition of ADL is composed by several stages, including data acquisition, data processing, and artificial intelligence methods. The artificial intelligence methods used are related to pattern recognition, and this study focuses on the use of Artificial Neural Networks (ANN). The data processing includes data cleaning, and the feature extraction techniques to define the inputs for the ANN. Due to the low processing power and memory of the mobile devices, they should be mainly used to acquire the data, applying an ANN previously trained for the identification of the ADL. The main purpose of this paper is to present a new method implemented with ANN for the identification of a defined set of ADL with a reliable accuracy. This paper also presents a comparison of different types of ANN in order to choose the type for the implementation of the final method. Results of this research probes that the best accuracies are achieved with Deep Learning techniques with an accuracy higher than 80%. Following the previous research studies [4][5][6], the recognition of the ADL is composed by several steps, such as the data acquisition, the data processing, composed by data cleaning, data imputation, and feature extraction, the data fusion, and the artificial intelligence methods for the concrete identification of the ADL. However, this study only uses the accelerometer sensor, removing some steps of the proposed architecture. Based on the assumption that the sensor was always acquiring the data, the final steps used are the data acquisition, the data cleaning, and the application of the artificial intelligence methods.During the last years, the recognition of ADL has been studies by several authors [7][8][9][10][11][12], verifying that the Artificial Neural Networks (ANN) are widely used. This paper proposes the creation of a method for the recognition of ADL using accelerometer, comparing three types of ANN, such as Multilayer Perception (MLP) with Backpropagation, Feedforward neural network with Backpropagation, and Deep Learning, in order to verify the method that achieves the best accuracy in the recognition of running, walking, going upstairs, going downstairs, and standing. The datasets are composed with raw accelerometer data acquired by individual with ages ranged between 16 and 60 years old and different lifestyles, with a mobile device in the pocket. For the implementation of these types of ANN are used several datasets with different sets of features, identifying the best features to increase the accuracy of the recognition, and three Java libraries are used for the implementation of the different methods, such as Neuroph [13], Encog [14], and DeepLearning4j [15], achieving the best accuracy with Deep Learning methods.The remaining sections of this paper are organized as follows: Section 2 presents a brief literature review re...

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“…Much work has been done in the last decade in the field of telemedicine with respect to the home assistance of patients affected by chronic diseases and significant effort has been devoted in particular to wearable sensors for the detection of motion symptoms [20,21,22,23,24,25]. In the specific context of the gait analysis and FOG detection in PD, inertial measurement units (IMU) are mainly used [24,25,26,27,28,29,30,31,32,33,34,35,36,37,38,39,40,41,42,43,44,45,46,47]. In very few cases, different signals are considered.…”

Section: Introductionmentioning

confidence: 99%

Wearable Sensors System for an Improved Analysis of Freezing of Gait in Parkinson’s Disease Using Electromyography and Inertial Signals

Mazzetta

Zampogna

Suppa

et al. 2019

Sensors

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We propose a wearable sensor system for automatic, continuous and ubiquitous analysis of Freezing of Gait (FOG), in patients affected by Parkinson’s disease. FOG is an unpredictable gait disorder with different clinical manifestations, as the trembling and the shuffling-like phenotypes, whose underlying pathophysiology is not fully understood yet. Typical trembling-like subtype features are lack of postural adaptation and abrupt trunk inclination, which in general can increase the fall probability. The targets of this work are detecting the FOG episodes, distinguishing the phenotype and analyzing the muscle activity during and outside FOG, toward a deeper insight in the disorder pathophysiology and the assessment of the fall risk associated to the FOG subtype. To this aim, gyroscopes and surface electromyography integrated in wearable devices sense simultaneously movements and action potentials of antagonist leg muscles. Dedicated algorithms allow the timely detection of the FOG episode and, for the first time, the automatic distinction of the FOG phenotypes, which can enable associating a fall risk to the subtype. Thanks to the possibility of detecting muscles contractions and stretching exactly during FOG, a deeper insight into the pathophysiological underpinnings of the different phenotypes can be achieved, which is an innovative approach with respect to the state of art.

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Mobile Devices For The Real Time Detection Of Specific Human Motion Disorders

Cited by 28 publications

References 34 publications

A Non-Invasive Medical Device for Parkinson’s Patients with Episodes of Freezing of Gait

A Non-Invasive Medical Device for Parkinson’s Patients with Episodes of Freezing of Gait

Pattern recognition techniques for the identification of Activities of Daily Living using mobile device accelerometer

Wearable Sensors System for an Improved Analysis of Freezing of Gait in Parkinson’s Disease Using Electromyography and Inertial Signals

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