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1. Fall Prediction for Bipedal Robots: The Standing Phase

   https://doi.org/10.1109/ICRA57147.2024.10611387  

   Mungai, M Eva; Prabhakaran, Gokul; Grizzle, Jessy W (May 2024, IEEE)

   This paper presents a novel approach to fall prediction for bipedal robots, specifically targeting the detection of potential falls while standing caused by abrupt, incipient, and intermittent faults. Leveraging a 1D convolutional neural network (CNN), our method aims to maximize lead time for fall prediction while minimizing false positive rates. The proposed algorithm uniquely integrates the detection of various fault types and estimates the lead time for potential falls. Our contributions include the development of an algorithm capable of detecting abrupt, incipient, and intermittent faults in full-sized robots, its implementation using both simulation and hardware data for a humanoid robot, and a method for estimating lead time. Evaluation metrics, including false positive rate, lead time, and response time, demonstrate the efficacy of our approach. Particularly, our model achieves impressive lead times and response times across different fault scenarios with a false positive rate of 0. The findings of this study hold significant implications for enhancing the safety and reliability of bipedal robotic systems. 

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2. Personalized Fall Detection System

   Ngu, Anne H; Metsis, Vangelis; Coyne, Shaun; Chung, Brian; Pai, Rachel; Chang, Joshua (January 2020, 5th IEEE PerCom Workshop on Pervasive Health Technologies)

   This paper explores the personalization of smartwatch-based fall detection models trained using a combination of deep neural networks with ensemble techniques. Deep neural networks face practical challenges when used for fall detection, which in general tend to have limited training samples and imbalanced datasets. Moreover, many motions generated by a wrist-worn watch can be mistaken for a fall. Obtaining a large amount of real-world labeled fall data is impossible as fall is a rare event. However, it is easy to collect a large number of non-fall data samples from users. In this paper, we aim to mitigate the scarcity of training data in fall detection by first training a generic deep learning ensemble model, optimized for high recall, and then enhancing the precision of the model, by collecting personalized false positive samples from individual users, via feedback from the SmartFall App. We performed real-world experiments with five volunteers and concluded that a personalized fall detection model significantly outperforms generic fall detection models, especially in terms of precision. We further validated the performance of personalization by using a new metric for evaluating the accuracy of the model via normalizing false positive rates with regard to the number of spikes of acceleration over time. 

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3. Transform Waveforms into Signature Vectors for General-purpose Incipient Fault Detection

   https://doi.org/10.1109/TPWRD.2022.3151110  

   Weng, Yang; Cui, Qiushi; Guo, Muhao (February 2022, IEEE Transactions on Power Delivery)

   Power system equipment presents special signatures at the incipient stage of faults. As more renewables are integrated into the systems, these signatures are harder to detect. If faults are detected at an early stage, economical losses and power outages can be avoided in modern power grids. Many researchers and power engineers have proposed a series of signature-specific methods for one type of equipment's waveform abnormality. However, conventional methods are not designed to identify multiple types of incipient faults (IFs) signatures at the same time. Therefore, we develop a general-purpose IF detection method that detects waveform abnormality stemming from multiple types of devices. To avoid the computational burden of the general-purpose IF detection method, we embed the abnormality signatures into a vector and develop a pre-training model (PTM) for machine understanding. In the PTM, signal "words," "sentences," and "dictionaries" are designed and proposed. Through the comparison with a machine learning classifier and a simple probabilistic language model, the results show a superior detection performance and reveal that the training radius is highly related to the size of abnormal waveforms. 

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4. Variable Window and Deadline-Aware Sensor Attack Detector for Automotive CPS

   Akowuah, Francis; Fletcher, Kenneth; Kong, Fanxin (May 2023, 26th International Symposium On Real-Time Distributed Computing (ISORC))

   Cyber-physical systems (CPS) are susceptible to physical attacks, and researchers are exploring ways to detect them. One method involves monitoring the system for a set duration, known as the time-window, and identifying residual errors that exceed a predetermined threshold. However, this approach means that any sensor attack alert can only be triggered after the time-window has elapsed. The length of the time-window affects the detection delay and the likelihood of false alarms, with a shorter time-window leading to quicker detection but a higher false positive rate, and a longer time-window resulting in slower detection but a lower false positive rate. While researchers aim to choose a fixed time-window that balances a low false positive rate and short detection delay, this goal is difficult to attain due to a trade-off between the two. An alternative solution proposed in this paper is to have a variable time-window that can adapt based on the current state of the CPS. For instance, if the CPS is heading towards an unsafe state, it is more crucial to reduce the detection delay (by decreasing the time-window) rather than reducing the false alarm rate, and vice versa. The paper presents a sensor attack detection framework that dynamically adjusts the time-window, enabling attack alerts to be triggered before the system enters dangerous regions, ensuring timely detection. This framework consists of three components: attack detector, state predictor, and window adaptor. We have evaluated our work using real-world data, and the results demonstrate that our solution improves the usability and timeliness of time-window-based attack detectors. 

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5. Model Free Method of Screening Training Data for Adversarial Datapoints Through Local Lipschitz Quotient Analysis

   https://doi.org/10.1109/LRA.2024.3483628  

   Kamienski, Emily; Asada, Harry (December 2024, IEEE Robotics and Automation Letters)

   It is often challenging to pick suitable data features for learning problems. Sometimes certain regions of the data are harder to learn because they are not well characterized by the selected data features. The challenge is amplified when resources for sensing and computation are limited and time-critical, yet reliable decisions must be made. For example, a robotic system for preventing falls of elderly people needs a real-time fall predictor, with low false positive and false negative rates, using a simple wearable sensor to activate a fall prevention mechanism. Here we present a methodology for assessing the learnability of data based on the Lipschitz quotient.We develop a procedure for determining which regions of the dataset contain adversarial data points, input data that look similar but belong to different target classes. Regardless of the learning model, it will be hard to learn such data. We then present a method for determining which additional feature(s) are most effective in improving the predictability of each of these regions. This is a model-independent data analysis that can be executed before constructing a prediction model through machine learning or other techniques. We demonstrate this method on two synthetic datasets and a dataset of human falls, which uses inertial measurement unit signals. For the fall dataset, we identified two groups of adversarial data points and improved the predictability of each group over the baseline dataset, as assessed by Lipschitz, by using 2 different sets of features. This work offers a valuable tool for assessing data learnability that can be applied to not only fall prediction problems, but also other robotics applications that learn from data. 

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