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Wearable IoT and Artificial Intelligence Techniques for Leveraging the Human Activity Analysis

Lina Sheker

Lina Sheker

Department of electrical and computer engineering, Altinbaş UniversityIstanbul, Turkey
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Sheker, Lina
, 
Vishwanath Petli

Vishwanath Petli

Department Electronics and Communication Engineering, H.K.E. Society’s Sir M Visvesvaraya College of EngineeringRaichur, India
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Petli, Vishwanath
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K. Satish Reddy

K. Satish Reddy

Department of Electronics and Communication Engineering, Hyderabad Institute of Technology and ManagementHyderabad, India
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Satish Reddy, K.
  
15 jun 2024
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Categoría del artículo: Article
Publicado en línea: 15 jun 2024
Páginas: 31 - 45
Recibido: 05 mar 2024
Aceptado: 05 mar 2024
DOI: https://doi.org/10.2478/jsiot-2024-0003
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© 2023 Lina Sheker et al., published by Sciendo
This work is licensed under the Creative Commons Attribution 4.0 International License.

Figure 1:

Proposed Architecture
Proposed Architecture

Figure 2:

Activity Distribution in the WISDM Dataset
Activity Distribution in the WISDM Dataset

Figure 3:

CNN Architecture
CNN Architecture

Figure 4:

LSTM Architecture
LSTM Architecture

Figure 5:

Comparative Analysis with Varying DL models based on testing accuracy
Comparative Analysis with Varying DL models based on testing accuracy

Figure 6:

ROC Curves for the Different Models in recognizing activities.
ROC Curves for the Different Models in recognizing activities.

Figure 7:

Loss Validation Curve for the Proposed model
Loss Validation Curve for the Proposed model

Comparative Analysis between the Different Models in recognizing activities_

Algorithm Accuracy (%) Sensitivity (%) Specificity (%) Precision (%) F1-Score (%)
ANN 84.6% 85.8% 83.6% 83.4% 85.9%
GRU 86.1% 86.4% 85.6% 85.8% 85.8%
LSTM 88.3% 87.5% 86.7% 86.7% 87.8%
CNN 92% 91.3% 89.1% 90% 91.8%
Proposed Model 99.4% 98.6% 99.3% 99% 98.2%

Dataset Attribute Description

Attribute Type Description
User Nominal Identifier for participants, ranging from 1 to 36.
Activity Nominal The activity performed, classified into six categories: Walking, Jogging, Sitting, Standing, Upstairs, Downstairs.
Timestamp Numeric Device uptime in nanoseconds, representing the timing of recorded motion.
x-Acceleration Numeric Acceleration along the x-axis in m/s2, including gravitational acceleration.
y-Acceleration Numeric Acceleration along the y-axis in m/s2, including gravitational acceleration.
z-Acceleration Numeric Acceleration along the z-axis in m/s2, including gravitational acceleration.

Evaluation Metrics utilized for assessment

SL.NO Performance Measures Expression
1 Accuracy TP+TNTP+TN+FP+FN {{TP + TN} \over {TP + TN + FP + FN}}
2 Recall TPTP+FN×100 {{{\rm{TP}}} \over {{\rm{TP}} + {\rm{FN}}}} \times 100
3 Specificity TNTN+FP {{TN} \over {TN + FP}}
4 Precision TNTP+FP {{TN} \over {TP + FP}}
5 F1-Score 2.Precison ∗ RecallPrecison + Recall 2.{{Precison\, * \,{Recall}} \over {Precision\, + \, {Recall}}}
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Inglés
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Ingeniería, Introducciones y reseñas, Historia de la Ingeniería, Ingeniería eléctrica, Fundamentos de ingeniería eléctrica, Electrónica, Tecnologías de la información
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