IMU Based Fall Detection — Biomedical Instrumentation & Clinical Sensing

Abstract — IMU Based Fall Detection

Imu Based Fall Detection: Biomedical Engineering, Medical Electronics & Bio-Signal Modeling Integrated Clinical Investigation: Bio-Sensors · Analog Front-Ends (AFE) · Diagnostic Waveforms · DSP Algorithms · ~12059 Words Chapter 1: Electrophysiological Foundations, Action Potentials & Clinical Significance The growing demand for continuous physiological monitoring and human-machine interaction in real-world settings calls for wearable platforms that are flexible, low-power, and capable of on-device intelligence. This work presents BioGAP -Ultra , an advanced multimodal biosensing platform that supports synchronized acquisition of diverse electrophysiological and hemodynamic signals such as EEG, EMG, ECG, and PPG while enabling embedded AI processing at state-of-the-art energy efficiency. BioGAP-Ultra is a major extension of our previous design, BioGAP , aimed at meeting the rapidly growing requirements of wearable biosensing applications. It features (i) increased on-device storage ( × \times 2 SRAM, × \times 4 FLASH), (ii) improved wireless connectivity (1.4 Mbit/s bandwidth, × \times 4 higher than BioGAP), (iii) enhanced number of signal modalities (from 3 to 5) and analog input channels ( × \times 2). Further, it is complemented by a complete real-time visualization and analysis software suite complements the hardware design, providing access to raw data and real-time configurability on a mobile phone. Electrical characterization and multiple case studies confirm the platform’s robustness, configurability, and suitability for real-world multimodal biosignal acquisition and edge intelligence. Finally, we demonstrate the system’s versatility through integration into various wearable form factors: an EEG-PPG headband consuming 32.8 mW, an EMG sleeve at 26.7 mW, and an ECG-PPG chest band requiring only 9.3 mW, tailored for diverse biosignal applications. All hardware and software design files are also released open-source with a permissive license.

Sensor Interfacing & Analog Front-End Conditioning

Recent advances in hardware miniaturization and embedded AI are driving the integration of biosignal monitoring into out-of-the-lab environments. Compact, low-power wearable devices now allow long-term recording of high-quality biosignals in daily life conditions, driving a global market projected to grow beyond USD 50 billion by 2030 . However, many existing platforms remain limited to single-sensor acquisition or offload processing externally, reducing robustness and missing out on opportunities for context-aware real-time interaction.

Bio-Signal Waveform Analysis & Simulation Findings

Multimodal biosignal acquisition has emerged as a key strategy to address these challenges. Combining complementary signals such as EEG ( EEG ) for cortical activity, EMG ( EMG ) for muscular function, ECG ( ECG ) for cardiac dynamics, and PPG ( PPG ) for peripheral hemodynamics improves the estimation of physiological states and mitigates the impact of motion artifacts and noise . However, the design of synchronized, wearable multimodal systems is still underexplored, especially in the context of multimodal BCI ( BCI )s , where EEG poses additional challenges such as guaranteeing high signal quality with the low SNR ( SNR ). This paper targets tasks requiring multimodal, multichannel biosignal acquisition (including EEG) and in-situ processing.

Scope & Clinical Applications

• Real-time multi-parameter vital signs telemetry (ECG, Heart Rate, SpO2, Temperature, Respiration)
• Low-noise Analog Front-End (AFE) conditioning with active Driven Right Leg (DRL) 50 Hz cancellation
• Digital signal processing in MATLAB: Pan-Tompkins QRS detection, FIR/IIR notch, and Continuous Wavelet Transforms
• Finite element tissue bio-electromagnetics and orthopedic mechanical simulations in COMSOL & ANSYS
• Complete Arduino firmware .INO sketches, circuit schematics, and university dissertation reports