Home / Vol. 38 No. E1 (2026): SPECIAL VOLUME (OCT,2026) - The ICTs and their impact on society. Technical Track TICEC 2026

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Pulneo: Gamified Mobile Prototype with Acoustic Respiratory Activity Detection for Home-Based Physiotherapy

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Abstract

Home-based respiratory physiotherapy requires repetition and feedback, yet many sessions are performed without direct supervision. This paper presents Pulneo, a gamified mobile prototype that detects respiratory activity on-device from acoustic signals and maps it to visual biofeedback during guided exercises. The model was trained on 69,634 balanced windows: 34,817 respiratory-activity windows from 1,061 healthy Coswara subjects and 34,817 negative windows from 5,608 UrbanSound8K files, using a group-wise 70/15/15 split with no subject or source-file overlap. On 10,347 test windows, the TensorFlow Lite INT8 model achieved 97.64% accuracy, 98.09% precision, 97.18% recall, 97.63% F1-score, and 99.65% ROC-AUC. Quantization reduced the model from 88,488 to 31,600 bytes (64.29%) while preserving 99.55% decision agreement with TFLite FP32. The application interprets the output as P(breathing) and applies a threshold calibrated on validation data (P ≥ 0.29). Pulneo does not distinguish inhalation from exhalation and has not been clinically validated; the findings support technical performance, not therapeutic efficacy. An author who is a physiotherapist with a master's degree in respiratory therapy conducted an internal conceptual review to delimit intended use, biofeedback interpretation, and safety requirements for future evaluations.

Keywords

gamification biofeedback respiratory physiotherapy TensorFlow Lite MFCC TinyML edge computing respiratory activity detection

How to Cite

Viñansaca Cabrera, M. G., & Valladarez Muñoz, D. F. (2026). Pulneo: Gamified Mobile Prototype with Acoustic Respiratory Activity Detection for Home-Based Physiotherapy. Revista Tecnológica - ESPOL, 38(E1), 136-152. https://doi.org/10.37815/rte.v38nE1.1602
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References

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