Abstract-- Real-time fall detection is crucial for enabling timely interventions and mitigating the severe health consequences of falls, particularly in older adults. However, existing methods often rely on simulated data or assumptions such as prior knowledge of fall events, limiting their real-world applicability. Practical deployment also requires efficient computation and robust evaluation metrics tailored to continuous monitoring. This paper presents a real-time fall detection framework for continuous monitoring without prior knowledge of fall events. Using over 60 hours of inertial measurement unit (IMU) data from the FARSEEING real-world falls dataset, we employ recent efficient classifiers to compute fall probabilities in streaming mode. To enhance robustness, we introduce a cost-sensitive learning strategy that tunes the decision threshold using a cost function reflecting the higher risk of missed falls compared to false alarms. Unlike many methods that achieve high recall only at the cost of precision, our framework achieved Recall of 1.00, Precision of 0.84, and an F These results demonstrate that cost-sensitive threshold tuning enhances the robustness of accelerometer-based fall detection. They also highlight the potential of our computationally efficient framework for deployment in real-time wearable sensor systems for continuous monitoring. A fall is an event that results in a person coming to rest unintentionally on the ground, floor, or other lower level [1].

Fall risk prediction among hospitalized patients is a critical aspect of patient safety in clinical settings, and accurate models can help prevent adverse events. The Hester Davis Score (HDS) is commonly used to assess fall risk, with current clinical practice relying on a threshold-based approach. In this method, a patient is classified as high-risk when their HDS exceeds a predefined threshold. However, this approach may fail to capture dynamic patterns in fall risk over time. In this study, we model the threshold-based approach and propose two machine learning approaches for enhanced fall prediction: One-step ahead fall prediction and sequence-to-point fall prediction. The one-step ahead model uses the HDS at the current timestamp to predict the risk at the next timestamp, while the sequence-to-point model leverages all preceding HDS values to predict fall risk using deep learning. We compare these approaches to assess their accuracy in fall risk prediction, demonstrating that deep learning can outperform the traditional threshold-based method by capturing temporal patterns and improving prediction reliability. These findings highlight the potential for data-driven approaches to enhance patient safety through more reliable fall prevention strategies.

Computer vision technology, which involves analyzing images and videos captured by cameras through deep learning algorithms, has significantly advanced the field of human fall detection. This study focuses on the application of the YoloV8 Nano model in identifying fall incidents within passenger elevators, a context that presents unique challenges due to the enclosed environment and varying lighting conditions. By training the model on a robust dataset comprising over 10,000 images across diverse elevator types, we aim to enhance the detection precision and recall rates. The model's performance, with an 85% precision and 82% recall in fall detection, underscores its potential for integration into existing elevator safety systems to enable rapid intervention.

In recent years, as the population ages, falls have increasingly posed a significant threat to the health of the elderly. We propose a real-time fall detection system that integrates the inertial measurement unit (IMU) of a smartphone with optimized Wi-Fi channel state information (CSI) for secondary validation. Initially, the IMU distinguishes falls from routine daily activities with minimal computational demand. Subsequently, the CSI is employed for further assessment, which includes evaluating the individual's post-fall mobility. This methodology not only achieves high accuracy but also reduces energy consumption in the smartphone platform. An Android application developed specifically for the purpose issues an emergency alert if the user experiences a fall and is unable to move. Experimental results indicate that the CSI model, based on convolutional neural networks (CNN), achieves a detection accuracy of 99%, \revised{surpassing comparable IMU-only models, and demonstrating significant resilience in distinguishing between falls and non-fall activities.

The increasing shortage of nursing staff and the acute risk of falls in nursing homes pose significant challenges for the healthcare system. This study presents the development of an automated fall detection system integrated into care beds, aimed at enhancing patient safety without compromising privacy through wearables or video monitoring. Mechanical vibrations transmitted through the bed frame are processed using a short-time Fourier transform, enabling robust classification of distinct human fall patterns with a convolutional neural network. Challenges pertaining to the quantity and diversity of the data are addressed, proposing the generation of additional data with a specific emphasis on enhancing variation. While the model shows promising results in distinguishing fall events from noise using lab data, further testing in real-world environments is recommended for validation and improvement. Despite limited available data, the proposed system shows the potential for an accurate and rapid response to falls, mitigating health implications, and addressing the needs of an aging population. This case study was performed as part of the ZIM Project. Further research on sensors enhanced by artificial intelligence will be continued in the ShapeFuture Project.

Falls among individuals, especially the elderly population, can lead to serious injuries and complications. Detecting impact moments within a fall event is crucial for providing timely assistance and minimizing the negative consequences. In this work, we aim to address this challenge by applying thorough preprocessing techniques to the multisensor dataset, the goal is to eliminate noise and improve data quality. Furthermore, we employ a feature selection process to identify the most relevant features derived from the multisensor UP-FALL dataset, which in turn will enhance the performance and efficiency of machine learning models. We then evaluate the efficiency of various machine learning models in detecting the impact moment using the resulting data information from multiple sensors. Through extensive experimentation, we assess the accuracy of our approach using various evaluation metrics. Our results achieve high accuracy rates in impact detection, showcasing the power of leveraging multisensor data for fall detection tasks. This highlights the potential of our approach to enhance fall detection systems and improve the overall safety and well-being of individuals at risk of falls.

Falls among the elderly are a major health concern, frequently resulting in serious injuries and a reduced quality of life. In this paper, we propose "BlockTheFall," a wearable device-based fall detection framework which detects falls in real time by using sensor data from wearable devices. To accurately identify patterns and detect falls, the collected sensor data is analyzed using machine learning algorithms. To ensure data integrity and security, the framework stores and verifies fall event data using blockchain technology. The proposed framework aims to provide an efficient and dependable solution for fall detection with improved emergency response, and elderly individuals' overall well-being. Further experiments and evaluations are being carried out to validate the effectiveness and feasibility of the proposed framework, which has shown promising results in distinguishing genuine falls from simulated falls. By providing timely and accurate fall detection and response, this framework has the potential to substantially boost the quality of elderly care.

Fall detection for the elderly is a well-researched problem with several proposed solutions, including wearable and non-wearable techniques. While the existing techniques have excellent detection rates, their adoption by the target population is lacking due to the need for wearing devices and user privacy concerns. Our paper provides a novel, non-wearable, non-intrusive, and scalable solution for fall detection, deployed on an autonomous mobile robot equipped with a microphone. The proposed method uses ambient sound input recorded in people's homes. We specifically target the bathroom environment as it is highly prone to falls and where existing techniques cannot be deployed without jeopardizing user privacy. The present work develops a solution based on a Transformer architecture that takes noisy sound input from bathrooms and classifies it into fall/no-fall class with an accuracy of 0.8673. Further, the proposed approach is extendable to other indoor environments, besides bathrooms and is suitable for deploying in elderly homes, hospitals, and rehabilitation facilities without requiring the user to wear any device or be constantly "watched" by the sensors.

It is well known that the quality and quantity of training data are significant factors which affect the development and performance of machine intelligence algorithms. Without representative data, neither scientists nor algorithms would be able to accurately capture the visual details of objects, actions or scenes. An evaluation methodology which filters data quality does not yet exist, and currently, the validation of the data depends solely on human factor. This study reviews several public datasets and discusses their limitations and issues regarding quality, feasibility, adaptation and availability of training data. A simple approach to evaluate (i.e. automatically "clean" samples) training data is proposed with the use of real events recorded on the YouTube platform. This study focuses on action recognition data and particularly on human fall detection datasets. However, the limitations described in this paper apply in virtually all datasets.