Retrospective Frailty Assessment in Older Adults Using Inertial Measurement Unit-Based Deep Learning on Gait Spectrograms

Abstract

Frailty is a common syndrome in the elderly, marked by an increased risk of negative health outcomes such as falls, disability and death. It is important to detect frailty early and accurately to apply timely interventions that can improve health results in older adults. Traditional evaluation methods often depend on subjective evaluations and clinical opinions, which might lack consistency. This research uses deep learning to classify frailty from spectrograms based on IMU data collected during gait analysis. The study retrospectively analyzed an existing IMU dataset. Gait data were categorized into Frail, PreFrail, and NoFrail groups based on clinical criteria. Six IMUs were placed on lower extremity segments to collect motion data during walking activities. The raw signals from accelerometers and gyroscopes were converted into time-frequency spectrograms. A convolutional neural network (CNN) trained solely on raw IMU-derived spectrograms achieved 71.4 % subject-wise accuracy in distinguishing frailty levels. Minimal preprocessing did not improve subject-wise performance, suggesting that the raw time-frequency representation retains the most salient gait cues. These findings suggest that wearable sensor technology combined with deep learning provides a robust, objective tool for frailty assessment, offering potential for clinical and remote health monitoring applications.

Keywords: IMU; classification; convolutional neural networks; frailty; gait; spectrogram.

Figure 3

Baseline processed accelerometer signals of IMU fixed on thigh, shank and foot segments.

Figure 4

Low pass filtered gyroscope and accelerometer signals of IMU fixed on thigh segment.

Figure 1

Multimodal deep learning model architecture.

Figure 2

Raw gyroscope and accelerometer signals of IMU fixed on thigh segment.

Figure 5

Spectrograms generated from accelerometer signals.

Figure 6

Subject-level classification confusion matrices comparing raw, processed, and filtered spectrogram models. Color intensity indicates classification accuracy (darker colors represent higher values).

Figure 7

Spectrogram-level classification confusion matrices comparing raw, processed and filtered spectrogram models. Color intensity indicates classification accuracy (darker colors represent higher values).

Figure 8

Effect of data leakage on test accuracy.