SACM - United States of America
Permanent URI for this collectionhttps://drepo.sdl.edu.sa/handle/20.500.14154/9668
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Item Restricted Deciphering Hand Movement Patterns During Driving Using Smartwatch Signals Without Ground Truth(University of Houston, 2025-02-07) Alghamdi, Huda; Pavlidis, IoannisWe developed a method to identify atypical hand movements in driving, some of which are associated with detachment from the steering wheel and, thus, physical distraction. We performed our data analysis on NUBI { a naturalistic dataset collected from a week-long observation of n=57 Texas drivers. NUBI features data from over 900 trips with a total duration of over 300 hours. Due to a lack of visual ground truth, we employed unsupervised learning methods. Thanks to the GPS data to our avail, we used information about the type of road (highway or city street) and the type of segment (straight or turn) to narrow our search space. In more detail, we extracted features from the drivers' smartwatch motion signals using Temporal Convolutional Autoencoder (TCNAE). Then, we fed these encoded features into a Density-Based Spatial Clustering of Applications with Noise (DBSCAN) algorithm. DBSCAN produced a main cluster and the remainder. The remainder is consistently associated with behaviors in turns and other atypical scenarios, such as queuing to pick up orders from fast-food dispensing windows. The characteristics of these atypical patterns are so distinct from the typical driving patterns (main cluster) that a random forest classi cation algorithm attained 99% area under the curve (AUC) performance in a ve-fold cross-validation test. Based on the kinematic constraints of the driver's hands, we developed a physics-based formula that associates elbow angles with gravitational acceleration values. We estimated the gravitational acceleration values that correspond to hand detachment from the steering wheel (i.e., extreme elbow angles). Applying these thresholds to the NUBI dataset, we found that such steering wheel detachment values arise just outside the dispensing windows of fast-food chains, where the drivers must pick up their orders. This nding validates our estimation method. In all, our approach not only nds atypical hand-motion patterns in driving but also pinpoints among these atypical patterns the patterns that involve hand detachment from the steering wheel. The latter are associated with physical distractions and crash risk under certain conditions. Notably, our approach achieves all these from smartwatch signals alone without any need to resort to visual ground truth from camera feeds. Given the ubiquity of smartwatches and the unavailability of cameras in car interiors, the practical implications of this development cannot be overestimated.25 0Item Restricted TOWARDS ROBUST SENSOR-BASED HUMAN ACTIVITY RECOGNITION IN REAL- WORLD ENVIRONMENTS(Saudi Digital Library, 2023-11-21) Alkhoshi, Enas; Rasheed, Khaled; Arabnia, Hamid; Maier, Frederick; Gay, JenniferHuman Activity Recognition (HAR) using wearable sensors has become a popular research area in recent years due to its potential applications in various fields, such as healthcare, fitness, security, and smart homes. Even though numerous HAR systems are being developed, it is still challenging to create one that can accurately identify and classify human activities in actual environments. This dissertation presents methods for recognizing human activity using a single accelerometer-based system. The research explores the two pillars of making wearable sensor-based HAR systems robust and reliable: a free-living dataset that represents real-world scenarios and a user-independent system. Towards enhancing the robustness of the sensor-based HAR system, we applied deep analysis to several machine-learning techniques and models for identifying human activity using a pseudo-free-living dataset obtained from 20 participants at the University of Georgia. We found that hierarchical meta-classifiers outperformed deep learning and classical models by 6% for classifying seven activities. We classified the metabolic equivalent (METs) levels of physical activities and achieved 80% inter-subject accuracy. We introduced model personalization, and it increased the accuracy to 87% by including 50% of the participant's data. This approach is promoted since it lowers the inter-subject variability of the dataset. We built a user-independent sensor-based human activity recognition system to explore the impact of using demographic data and anthropometric features to improve the classification of the metabolic equivalent (METs) level of physical activities based on free-living data. We used a wearable accelerometer dataset collected by 270 participants from different cities in the state of Georgia performing various physical activities. We found that including demographic data and anthropometric features in the models improves their accuracy in classifying MET levels. We built and modified a transformer's self-attention mechanism to analyze motion signals over time, which expresses individual relationships between signal levels within a time series. Furthermore, model personalization was able to reduce the dataset's inter-subject variability and raised accuracy to 94.84% by including only 30% of the participant's data in training. Achieving high performance for subject-independent systems remains challenging when using real-world data.8 0