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2024 ITU Kaleidoscope Academic Conference




           passed  to  the  proposed  module,  the  Person  localization  is   Table 1 – Speech classification
           achieved  with  a  confidence  score  of  0.75  and  further
                                                                     Emotion   Precision   Recall   F1-Score
           classified as fall detected.
                                                                      Angry     0.98     0.96     0.97
                                                                     Disgust    0.96     0.97     0.97
                                                                       Fear     0.96     0.97     0.96
                                                                      Happy     0.96     0.95     0.96
                                                                     Neutral    0.97     0.98     0.97
                                                                       Sad      0.96     0.97     0.97
                                                                     Surprise   0.97     0.98     0.98



                   Figure 3 Realtime testing input image




















                                                                    Figure 5 – Confusion matrix of voice model
                     Figure 4 - Dataset sample images
                                                              Validation  accuracy  has  emerged  as  a  pivotal  metric  in
           The  video  module  combines  state-of-the-art  techniques  in
           computer vision and deep learning to monitor the activity   assessing  the  voice  model's  proficiency.  This  metric
                                                              measured  the  proportion  of  correctly  classified  instances
           and posture of elderly individuals in real time. By integrating
           MobileNet-based  posture  detection  with  attention  and   within the validation dataset, serving as a reliable indicator
                                                              of the overall robustness and reliability of the voice model.
           hourglass models, the system ensures accurate and efficient
           identification  of  anomalies,  such  as  falls.  This  proactive   With  an  impressive  validation  accuracy  of  96.34%,  the
                                                              proposed system's voice model demonstrated a high level of
           approach to elderly care enhances the safety and well-being
           of individuals living independently, providing peace of mind   accuracy  and  proficiency  in  discerning  and  classifying
                                                              different  speech  patterns  and  emotions.  The  relative  plot
           to both users and their caregivers.
                                                              between  epochs  and  training  and  validation  accuracies  is
                                                              shown in Figure 6.
                              5.  RESULTS
                                                              Precision,  a  fundamental  metric  in  classification  tasks,
           5.1   Performance of voice-based model
                                                              provided  valuable  insights  into  the  accuracy  of  the  voice
                                                              model's  predictions.  Both  weighted  average  precision  and
           In  the  evaluation  of  the  proposed  system's  voice  model
                                                              macro average precision were evaluated to assess the model's
           performance,  several  key  metrics  were  meticulously   performance across all classes in a multi-class classification
           analyzed to ascertain its efficacy in real-world applications.
                                                              scenario  specific  to  voice  analysis.  The  weighted  average
           Among  these  metrics,  the  validation  loss  assumed   precision,  which  accounted  for  the  number  of  samples  in
           significance as it quantified the error between the predicted
                                                              each class, offered a comprehensive assessment of the voice
           labels and the actual labels for the validation dataset, offering
                                                              model's  overall  precision.  Similarly,  the  macro  average
           insights  into  the  model's  generalization  capabilities  and
                                                              precision  provided  an  unbiased  measure  of  the  model's
           predictive accuracy specific to voice analysis. The system   performance  across  all  classes,  regardless  of  class
           performance  measure  on  speech  classification  is  listed  in
                                                              imbalances.  With  both  metrics  yielding  a  consistent
           Table-1 and corresponding confusion matrix is depicted in   precision score of 96%, the proposed system's voice model
           Figure 5.
                                                              demonstrated  a  commendable  level  of  accuracy  and
                                                              reliability across diverse speech patterns and emotions.







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