Slips, trips, and falls (STF) remain critical occupational hazards, particularly in physically demanding sectors such as logistics, healthcare, and manufacturing. While wearable inertial measurement unit (IMU) technologies combined with advanced deep learning approaches have demonstrated high precision in detecting near-fall events, their practical deployment is hindered by system complexity, low user compliance, and high costs due to extensive sensor setups. This study addresses these limitations by systematically evaluating sensor reduction strategies aiming at preserving detection accuracy while enhancing practicality and affordability. Three strategies: balanced sensor reduction, unilateral reduction, and reduction to the core, reducing sensor placements to biomechanically significant anatomical sites, were tested using the comprehensive Prev-Fall dataset, which captures real-world STF scenarios. Four established deep learning models (CNNs, ResNets, DeepConvLSTMs, and InceptionTime) were applied to assess detection performance across varying sensor configurations. Results indicate that targeted sensor placement, particularly at the pelvis and lower limbs, can maintain or even surpass the performance of conventional setups. Remarkably, a configuration using only two IMUs achieved over 80% accuracy and a macro F1-score exceeding 0.8. In general, reduction to less than three sensors led to notable misclassifications, especially between similar STF events. Furthermore, DeepConvLSTMs outperformed other architectures under minimal sensor conditions, outlining the importance of model–hardware alignment. These findings provide empirically grounded guidelines for cost-effective, wearable solutions that promote real-world feasibility and user acceptance in occupational safety applications.