Synthetic fall data from LLMs helps LSTM fall detectors on low-frequency waist datasets (UMAFall +56.83%) but hurts on high-frequency or wrist datasets; diffusion data matches real data best yet does not reliably improve detection.
Fall Detection using Knowledge Distillation Based Long short-term memory for Offline Embedded and Low Power Devices
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abstract
This paper presents a cost-effective, low-power approach to unintentional fall detection using knowledge distillation-based LSTM (Long Short-Term Memory) models to significantly improve accuracy. With a primary focus on analyzing time-series data collected from various sensors, the solution offers real-time detection capabilities, ensuring prompt and reliable identification of falls. The authors investigate fall detection models that are based on different sensors, comparing their accuracy rates and performance. Furthermore, they employ the technique of knowledge distillation to enhance the models' precision, resulting in refined accurate configurations that consume lower power. As a result, this proposed solution presents a compelling avenue for the development of energy-efficient fall detection systems for future advancements in this critical domain.
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cs.CL 1years
2025 1verdicts
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AI-Generated Fall Data: Assessing LLMs and Diffusion Model for Wearable Fall Detection
Synthetic fall data from LLMs helps LSTM fall detectors on low-frequency waist datasets (UMAFall +56.83%) but hurts on high-frequency or wrist datasets; diffusion data matches real data best yet does not reliably improve detection.