On modern construction sites, guidance and automation systems are increasingly applied to excavators. Recently, studies have been actively conducted to compare the estimation results of the bucket tip with the motion measurement method of the boom, stick, and bucket and the sensor selection. This study selected the method of measuring the cylinder length of boom, stick, and bucket, and the method of directly measuring the boom, arm, and bucket, which are commonly used in guidance and automation systems. A low-cost sensor that can be attached and detached to the excavator in modular form was selected to apply the above methods to commercial excavator. After the sensor selection, hardware and excavator simulation models for sensor measurements were constructed. Finally, the trajectory of the bucket tip was compared and analyzed through graphs and simulation results when the boom, stick, and bucket were independently rotated one by one, or together. The results gives a guideline on what kinds of sensors would be better in machine guidance or controlling an excavator according to given external environments.

Gait is intricately linked to human health, with key gait indexes showing strong correlations. To overcome limitations of flat-surface-focused evaluations, we propose a Modified Gait Index (MGI) that adapts to diverse terrains such as ramps and stairs. Gait data were collected from 15 healthy adults using motion capture, IMU (Inertial Measurement Unit), EMG (Electromyography), and force plates. For each terrain, principal component analysis (PCA) identified key contributing features based on loading vectors and contribution scores. MGI formulas were derived by combining these features with clinically relevant variables, including stance/swing ratio and joint flexion angles. Participants were divided into training and test groups based on PCA similarity, and generalization was evaluated using interquartile range-based normal thresholds. Across all terrains, the test group’s average GI values remained within the training-defined normal range: 1.05 - 2.82 for ramp ascent, 0.71 - 4.02 for ramp descent, 1.68 - 2.86 for stair ascent, 1.15 - 3.99 for stair descent. Although some test values approached quartile boundaries, no outliers were observed. These results demonstrate the MGI’s terrain-adaptive consistency and generalizability, supporting its application in exoskeleton control strategies for users with subclinical gait deviations or muscular overload.

On-device Human Activity Recognition (HAR) requires balancing accuracy and deployment efficiency on constrained hardware. We present Lightweight Human Activity Recognition (L-HAR), a controlled comparison of Baseline and Lightweight Temporal Convolutional Network (TCN), Transformer, and Long Short-Term Memory (LSTM) models using data sampled at $100\mathrm{Hz}$ from three inertial measurement units (IMUs) worn by one participant. Evaluations covered classification, model footprint, multiply–accumulate operations (MACs), offline CPU latency, Desktop/Raspberry Pi streaming, 8-bit integer (INT8) quantization, and software-estimated Raspberry Pi energy efficiency. Lightweighting reduced parameters by up to $95.5\%$, model size by $94.0\%$, and MACs by $87.6$–$95.5\%$, with accuracy reductions of $0.8$–$2.2$ percentage points (pp) and sub-millisecond offline latency for all Lightweight models. Baseline and Lightweight models sustained approximately $100\mathrm{Hz}$ message/inference rates on Desktop, whereas no Raspberry Pi configuration reached $100\mathrm{Hz}$ inference. Among Lightweight models, Transformer achieved $9.381\mathrm{ms}$ End-to-End (E2E) latency and $66.925\mathrm{Hz}$ inference. INT8 quantization changed accuracy and F1-score by less than $0.13$ pp; Quantized TCN achieved the best Raspberry Pi streaming result ($9.066\mathrm{ms}$ E2E; $69.928\mathrm{Hz}$) with an estimated $49.933\mathrm{mJ}$ per inference. These results demonstrate architecture- and backend-dependent deployment behavior and the need for direct target-platform evaluation.