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1. Why do Things Fall?

   Susskind, Leonard (February 2018, arXiv.org)
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. The Rise and Fall of Continents

   https://doi.org/10.3389/frym.2023.1114471  

   Whitney, Donna L.; Vanardois, Jonas; Taylor, Jennifer M.; Teyssier, Christian (November 2023, Frontiers for Young Minds)

   Continents are constantly moving, and sometimes they collide. When continents collide, they crumple, and thicken. Mountain ranges form in this “crash zone.” Deep rocks at the bottom of a crash zone are hot because they are so deep. Hot materials—even rocks—become weak. Hot rocks deep underground can move by flowing, even though they are mostly solid. First, they flow sideways and then upwards in large blobs. When upward-moving blobs are only a few kilometers below the surface of the Earth, they cool and harden into bell shapes (domes). Flowing rocks cause the crash zone to collapse and spread out. Continents go back to their pre-collision thickness. They are not exactly the same as before collision, though: some rocks that used to be at the bottom of the continents are now at the top! We can see these formerly deep parts of continents in rock domes all over the world. 

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4. Pervasive Pose Estimation for Fall Detection

   https://doi.org/10.1145/3478027  

   Luo, Jiaqing; Bai, Ruiyu; He, Suining; Shin, Kang G. (July 2022, ACM Transactions on Computing for Healthcare)

   Falls are the second leading cause of accidental or unintentional injuries/deaths worldwide. Accurate pose estimation using commodity mobile devices will help early detection and injury assessment of falls, which are essential for the first aid of elderly falls. By following the definition of fall, we propose a P ervasive P ose Est imation scheme for fall detection ( P \( ^2 \) Est ), which measures changes in tilt angle and height of the human body. For the tilt measurement, P \( ^2 \) Est leverages the pointing of the mobile device, e.g., the smartphone, when unlocking to associate the Device coordinate system with the World coordinate system. For the height measurement, P \( ^2 \) Est exploits the fact that the person’s height remains unchanged while walking to calibrate the pressure difference between the device and the floor. We have prototyped and tested P \( ^2 \) Est in various situations and environments. Our extensive experimental results have demonstrated that P \( ^2 \) Est can track the body orientation irrespective of which pocket the phone is placed in. More importantly, it enables the phone’s barometer to detect falls in various environments with decimeter-level accuracy. 

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5. Fall Detection via Inaudible Acoustic Sensing

   https://doi.org/10.1145/3478094  

   Lian, Jie; Yuan, Xu; Li, Ming; Tzeng, Nian-Feng (September 2021, Proceedings of the ACM on Interactive, Mobile, Wearable and Ubiquitous Technologies)

   The fall detection system is of critical importance in protecting elders through promptly discovering fall accidents to provide immediate medical assistance, potentially saving elders' lives. This paper aims to develop a novel and lightweight fall detection system by relying solely on a home audio device via inaudible acoustic sensing, to recognize fall occurrences for wide home deployment. In particular, we program the audio device to let its speaker emit 20kHz continuous wave, while utilizing a microphone to record reflected signals for capturing the Doppler shift caused by the fall. Considering interferences from different factors, we first develop a set of solutions for their removal to get clean spectrograms and then apply the power burst curve to locate the time points at which human motions happen. A set of effective features is then extracted from the spectrograms for representing the fall patterns, distinguishable from normal activities. We further apply the Singular Value Decomposition (SVD) and K-mean algorithms to reduce the data feature dimensions and to cluster the data, respectively, before input them to a Hidden Markov Model for training and classification. In the end, our system is implemented and deployed in various environments for evaluation. The experimental results demonstrate that our system can achieve superior performance for detecting fall accidents and is robust to environment changes, i.e., transferable to other environments after training in one environment. 

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