IoT BASED REAL-TIME VOICE ANALYSIS AND SMART MONITORING SYSTEM FOR DISABLED PEOPLE
Why this work is in the frame
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Bibliographic record
Abstract
This research emphasizes on Internet of Things (IoT) based affordable platforms to take proper and timely measures for disabled people. It is usually observed that people with different disabilities face difficulties in all walks of life, and adequate caretaking measures are not adopted in most cases. Real time and consistent caretaking for such handicapped people is a tedious task. This paper introduces an IoT based real time analysis and alerting system for the disabled people. The proposed standalone system consistently monitors voice activity of person and in case of any abnormality in analysis outcomes, the system automatically notifies concerned hospital or caregiver to prompt for the patient's situation. The voice features are extracted from analysed voice by employing Discrete Cosine Transform (DCT), and classified through Support Vector Machine (SVM). The prototype has been developed by using Raspberry Pi single board along with voice recording module, Wi-Fi module and LCD Screen. Cloud web services have been used to store the real time activity and performing voice analysis. Montreal Affective Voices (MAV) dataset has been utilized for training and testing of voice recognition. The designed system can be regarded as a rescue system for people suffering from various life threatening health conditions including bipolar disorder, hysteria, cardiac arrest, etc. An accuracy of 81.74% has been achieved for MAV dataset, whereas an accuracy of 67.90% is achieved for real time voice input as depicted in the analysed results.
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it