Figure 1:
Proposed Architecture
Figure 2:
Activity Distribution in the WISDM Dataset
Table 1:
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. |
Figure 3:
CNN Architecture
Figure 4:
LSTM Architecture
Table 2:
Evaluation Metrics utilized for assessment
| SL.NO | Performance Measures | Expression |
|---|---|---|
| 1 | Accuracy | |
| 2 | Recall | |
| 3 | Specificity | |
| 4 | Precision | |
| 5 | F1-Score |
Table 3:
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% |
Figure 5:
Comparative Analysis with Varying DL models based on testing accuracy
Figure 6:
ROC Curves for the Different Models in recognizing activities.
Figure 7:
Loss Validation Curve for the Proposed model