Page 143 - Kaleidoscope Academic Conference Proceedings 2024

Innovation and Digital Transformation for a Sustainable World

Accuracy

Figure 7 - Epoch vs. accuracy graph of video model
Figure 6 - Epoch vs. accuracy graph of voice model
Table 2 – Comparison of mean average precision
In addition to these performance metrics, the latency of the
voice model was evaluated to ensure its practicality in real- Sl. No Method Mean Average precision
time applications. The average latency for processing voice
1 Tan [15] 65.38%
inputs and generating predictions was measured at 1220
milliseconds across all classes, using Python's time package 2 Jain [7] 70.17%
for precise computation. This latency demonstrates the 3 Hao [16] 82.57%
system's ability to perform timely analysis and response, 4 Li [17] 82.37%
which is crucial for real-time monitoring and intervention. 5 Yinlong [6] 85.89%
6 Ours 88.00%
5.2 Performance of video-based model
The implementation of the video module in proposed system
In the evaluation of the video-based model, several key is targeted to detect and accurately interpret subtle visual
metrics were analyzed to gain comprehensive insights into cues (such as facial expressions, body language, and posture)
its performance and efficacy. Among these metrics, the at an early stage, before any further damage occurs or the
validation loss held paramount importance as it quantified health condition deteriorates. The impressive combination of
the degree of error between the model's predicted labels and high accuracy and low latency underscores the proposed
the actual labels for the validation dataset across each epoch system's practicality and effectiveness in real-world
of training. A lower validation loss indicated a higher level environment, particularly in enhancing elderly care through
of accuracy and precision in the model's predictions, prompt and reliable voice / video based anomaly detection
signifying an improved performance trajectory. Validation and support. The Raspberry Pi-based system implementation
accuracy is used as a key metric in assessing the model's facilitates versatile cost-effective healthcare solutions, which
effectiveness during the training phase. It measured the are easy to install and maintain in a home environment.
proportion of correctly classified instances within the
validation dataset, serving as a reliable indicator of the 6. CONCLUSION
overall robustness and reliability of the machine learning
model. The model's achievement of a validation accuracy of This paper presented an innovative approach to addressing
87.91% as shown in Figure 7, underscored its capability to the multifaceted challenges of elderly care through the
make accurate predictions across diverse data samples. design and implementation of an elderly wellness companion
system. The voice module demonstrated exceptional
Precision score computed on video model, provided valuable performance in analyzing speech patterns and detecting
insights into the accuracy of positive predictions generated emotional cues, achieving an impressive validation accuracy
by the model. Weighted average precision and macro of 96.34%. Through meticulous evaluation of key metrics
average precision were analyzed to gauge the model's such as validation loss and precision, the voice model
performance across all classes in a typical multi-class exhibited robustness and reliability in discerning and
classification scenario. With both metrics yielding a classifying different speech patterns and emotions. The voice
consistent precision score of 0.88, the model demonstrated a module acts as a preliminary check, which triggers the video
creditable level of accuracy and consistency across diverse module only when severity in state is detected, ensuring
classes, affirming its suitability for real-world applications in camera privacy of the elderly. Furthermore, the video model
healthcare and beyond. The comparative analysis of Mean complements the voice-based anomaly detection system by
Average Precision with existing systems is tabulated in providing additional insights into the physical state of elderly
Table 2. individuals. Through the seamless integration of voice and
video-based anomaly detection technologies, the proposed
system offers comprehensive and holistic care for elderly
individuals, promoting independence, safety, and well-being.

– 99 –