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Hanna Mueller

Publications and source records attributed to Hanna Mueller.

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Multi-Sensor Edge Angle Detection for Performance Analysis in Ski Jumping

In ski jumping, performance during the gliding phase depends on achieving an aerodynamic posture that maximizes the lift-to-drag ratio. In the V-style technique, the ski edge angle is a key determinant. Reducing the edge angle flattens the skis, increases their effective surface area, and improves aerodynamic lift, ultimately contributing to longer flight distances. Ski edge angles are biomechanically constrained by the limited range of ankle inversion. Current sensing solutions widely quantify these angles using multi-system approaches that combine sensor signals through geometric relations. Such configurations require instrumentation on both the boot and the ski, altering mass distribution, affecting balance during flight, and increasing system complexity. To overcome these limitations, this work presents a wearable sensing system that measures both boot inclination and ski edge angle without modifying the ski surface. Two ultrasonic Time of Flight (ToF) sensors and an in-shoe Inertial Measurement Unit (IMU) are integrated into a single boot-mounted unit. Edge angles are estimated by combining ultrasonic distance measurements with IMU data through geometric reconstruction of the boot-ski configuration. Laboratory experiments demonstrate an angle resolution of 0.4500{\deg}, a Mean Absolute Error (MAE) of 0.2640{\deg}, and a coefficient of determination exceeding 99\% when compared with reference measurements, indicating strong linear agreement between the two modalities. The system achieves an end-to-end latency of 30.31 ms, enabling real-time feedback suitable for athlete training, while consuming 1.28 mW of power. With a total weight of only 18.6 g the proposed system enables unobtrusive measurement of ski edge angle and boot orientation.

eess.SP

Flexible and Fully Quantized Ultra-Lightweight TinyissimoYOLO for Ultra-Low-Power Edge Systems

This paper deploys and explores variants of TinyissimoYOLO, a highly flexible and fully quantized ultra-lightweight object detection network designed for edge systems with a power envelope of a few milliwatts. With experimental measurements, we present a comprehensive characterization of the network's detection performance, exploring the impact of various parameters, including input resolution, number of object classes, and hidden layer adjustments. We deploy variants of TinyissimoYOLO on state-of-the-art ultra-low-power extreme edge platforms, presenting an in-depth a comparison on latency, energy efficiency, and their ability to efficiently parallelize the workload. In particular, the paper presents a comparison between a novel parallel RISC-V processor (GAP9 from Greenwaves) with and without use of its on-chip hardware accelerator, an ARM Cortex-M7 core (STM32H7 from ST Microelectronics), two ARM Cortex-M4 cores (STM32L4 from STM and Apollo4b from Ambiq), and a multi-core platform with a CNN hardware accelerator (Analog Devices MAX78000). Experimental results show that the GAP9's hardware accelerator achieves the lowest inference latency and energy at 2.12ms and 150uJ respectively, which is around 2x faster and 20% more efficient than the next best platform, the MAX78000. The hardware accelerator of GAP9 can even run an increased resolution version of TinyissimoYOLO with 112x112 pixels and 10 detection classes within 3.2ms, consuming 245uJ. To showcase the competitiveness of a versatile general-purpose system we also deployed and profiled a multi-core implementation on GAP9 at different operating points, achieving 11.3ms with the lowest-latency and 490uJ with the most energy-efficient configuration. With this paper, we demonstrate the suitability and flexibility of TinyissimoYOLO on state-of-the-art detection datasets for real-time ultra-low-power edge inference.

cs.CV