Assistive design for elderly living ambient using voice and gesture recognition system

Basanta, Haobijam; Huang, Yo-Ping; Lee, Tsu‐Tian

doi:10.1109/smc.2017.8122714

Cited by 20 publications

(8 citation statements)

References 21 publications

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“…Clustering is done to group unlabeled data [7]. Clustering can be defined as "the process of categorizing individual elements into groups whose members have similarities."…”

Section: Clusteringmentioning

confidence: 99%

Isolated Command Recognition Using MFCC and Clustering Algorithm

et al. 2020

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People who cannot walk by themselves need someone to carry them around on a wheelchair. A voice recognition system was developed to recognize a set of commands used by people with disability to control their wheelchair and devices around them. Voice samples of various commands from 8 different speakers were collected. The features of the collected samples were extracted using MFCC feature extraction technique. The MFCC (Mel-frequency cepstral coefficient) feature extraction technique involves pre-emphasis, framing, windowing, FFT, Mel-scale transformation operations. The major reason for choosing MFCC extraction is that the human ear has a response of a logarithmic scale and not a linear scale. Hence, all the framed voice samples are transformed to the Mel-scale with a logarithmic response and stored in the form of K-means clusters. Features extracted from test data are applied to the models developed for commands, and based on the minimum distance criterion, the model is selected to be closely associated with the respective voice command. The voice recognition system was developed for an 8-command model using MATLAB.

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“…Clustering is done to group unlabeled data [7]. Clustering can be defined as "the process of categorizing individual elements into groups whose members have similarities."…”

Section: Clusteringmentioning

confidence: 99%

Isolated Command Recognition Using MFCC and Clustering Algorithm

et al. 2020

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“…The hand gesture recognition systems can be classified into two approaches. The first approach is the contact approach using wearable gloves with a direct-attached sensor to provide physical response depending on the type of sensors such as flex sensors [4], gyroscope and accelerometer sensors [5], tactiles [6], and optic fibers [7]. All these sensors are used to give the output response related to capturing hand movement and position by detecting the proper coordination of the site of the forehand and fingers.…”

Section: Related Workmentioning

confidence: 99%

“…The current glove attached sensors for recognizing hand gestures, such as flex sensors [4], gyroscope and accelerometer sensors [5] are restrictive and uncomfortable for patients. In addition, they may cause skin damage to people who suffer from burns and http://www.nanobe.org those with sensitive skin.…”

Section: Introductionmentioning

confidence: 99%

Hand Gestures for Elderly Care Using a Microsoft Kinect

Oudah¹,

Al-Naji²,

Chahl³

2020

Nano BioMed ENG

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“…The performance of these systems is sensitive in the background, light conditions, and room geometry, and constrained by the field of view of the camera. On the other hand, sensorbased systems, worn on the wrist, leg, arm, chest, ankle, head and/or waist, employ accelerometers, gyroscopes and magnetometers, barometers, body sensor networks [2], electromyography sensors [3], or even sound sensors [4]. They have relatively small cost, are not particularly sensitive to the environmental conditions (e.g.…”

Section: Introductionmentioning

confidence: 99%

“…The original acceleration signal consists of 3 time series, one for each axis, of unknown length (since it depends on the particular gesture and the user who performed the movement) 4. We also examined various classifiers using different kernels, such as linear, sigmoid, radial, and parameters values.…”

mentioning

confidence: 99%

GestureKeeper: Gesture Recognition for Controlling Devices in IoT Environments

Sideridis

Zacharakis

Tzagkarakis

et al. 2019

2019 27th European Signal Processing Conference (EUSIPCO)

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This paper introduces and evaluates the Gesture-Keeper, a robust hand-gesture recognition system based on a wearable inertial measurements unit (IMU). The identification of the time windows where the gestures occur, without relying on an explicit user action or a special gesture marker, is a very challenging task. To address this problem, GestureKeeper identifies the start of a gesture by exploiting the underlying dynamics of the associated time series using a recurrence quantification analysis (RQA). RQA is a powerful method for nonlinear timeseries analysis, which enables the detection of critical transitions in the system's dynamical behavior. Most importantly, it does not make any assumption about the underlying distribution or model that governs the data. Having estimated the gesture window, a support vector machine is employed to recognize the specific gesture. Our proposed method is evaluated by means of a small-scale pilot study at FORTH and demonstrated that GestureKeeper can identify correctly the start of a gesture with a 87% mean balanced accuracy and classify correctly the specific hand-gesture with a mean accuracy of over 96%. To the best of our knowledge, GestureKeeper is the first automatic handgesture identification system based only on accelerometer. The performance analysis reveals the predictive power of the features and the system's robustness in the presence of additive noise. We also performed a sensitivity analysis to examine the impact of various parameters and a comparative analysis of different classifiers (SVM, random forests). Most importantly, the system can be extended to incorporate a large dictionary of gestures and operate without further calibration for a new user.Index Terms-Hand-gesture identification and recognition, inertial measurement unit, support vector machine, recurrence quantification analysis

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Assistive design for elderly living ambient using voice and gesture recognition system

Cited by 20 publications

References 21 publications

Isolated Command Recognition Using MFCC and Clustering Algorithm

Isolated Command Recognition Using MFCC and Clustering Algorithm

Hand Gestures for Elderly Care Using a Microsoft Kinect

GestureKeeper: Gesture Recognition for Controlling Devices in IoT Environments

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