Wireless transmitter of basic necessities for children with cerebral palsy

Andrade-B., Paul; Pinos-Velez, Eduardo; Ingavélez-Guerra, Paola

doi:10.1109/syseng.2015.7302792

Cited by 1 publication

(1 citation statement)

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“…8 and 9). Two kinds of modalities are managed when using multiple sensors: sensors in different places and diverse sensor types [29][30][31][32]. For each sort of modality, considered simple pre-processing and aggregated the inputs from multiple sensors in different poses to capture structural dependency between them.…”

Section: Methodology 41 2d Cnn Modelmentioning

confidence: 99%

Efficient Gait Analysis Using Deep Learning Techniques

Monica¹,

Parvathi²

2023

Computers, Materials &Amp; Continua

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Human Activity Recognition (HAR) has always been a difficult task to tackle. It is mainly used in security surveillance, human-computer interaction, and health care as an assistive or diagnostic technology in combination with other technologies such as the Internet of Things (IoT). Human Activity Recognition data can be recorded with the help of sensors, images, or smartphones. Recognizing daily routine-based human activities such as walking, standing, sitting, etc., could be a difficult statistical task to classify into categories and hence 2-dimensional Convolutional Neural Network (2D CNN) MODEL, Long Short Term Memory (LSTM) Model, Bidirectional long short-term memory (Bi-LSTM) are used for the classification. It has been demonstrated that recognizing the daily routine-based on human activities can be extremely accurate, with almost all activities accurately getting recognized over 90% of the time. Furthermore, because all the examples are generated from only 20 s of data, these actions can be recognised fast. Apart from classification, the work extended to verify and investigate the need for wearable sensing devices in individually walking patients with Cerebral Palsy (CP) for the evaluation of chosen Spatio-temporal features based on 3D foot trajectory. Case-control research was conducted with 35 persons with CP ranging in weight from 25 to 65 kg. Optical Motion Capture (OMC) equipment was used as the referral method to assess the functionality and quality of the foot-worn device. The average accuracy ± precision for stride length, cadence, and step length was 3.5 ± 4.3, 4.1 ± 3.8, and 0.6 ± 2.7 cm respectively. For cadence, stride length, swing, and step length, people with CP had considerably high inter-stride variables. Foot-worn sensing devices made it easier to examine Gait Spatio-temporal data even without a laboratory set up with high accuracy and precision about gait abnormalities in people who have CP during linear walking.

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Section: Methodology 41 2d Cnn Modelmentioning

confidence: 99%