Enhancing Wearable Fall Detection System via Synthetic Data by Minakshi Debnath, Sana Alamgeer, Md Shahriar Kabir, Anne H. Ngu

“…To address this, we explore methods for generating realistic synthetic multivariate fall data to supplement limited real-world samples collected from three fall-related datasets: SmartFallMM, UniMib, and K-Fall. …”
Get full text

BLE Signal Processing and Machine Learning for Indoor Behavior Classification by Yi-Shiun Lee, Yong-Yi Fanjiang, Chi-Huang Hung, Yung-Shiang Huang

Subjects: Get full text

MENTAL BARRIERS TO REDUCE VULNERABILITY TO INJURY DURING A FALL: AN ELEMENTARY ISSUE OF PERSONAL SAFETY IN A GLOBAL CIVILIZATION by Bartłomiej Gąsienica-Walczak, Artur Kalina, Artur Litwiniuk, Joanna Baj-Korpak

“…The child’s natural ability to protect their hands and head during an unintentional fall begins to be lost by the age of three. Therefore, learning how to fall safely is the surest of ways to reduce the number of people who would spend the rest of their lives in disability as a result of injuries sustained in a fall.…”
Get full text

QI initiative to reduce the number of inpatient falls in an acute hospital Trust by Jennifer Allison, Michelle Boot, Jack Maguire, Gemma O'Driscoll

“…Inpatient falls are one of the most frequent concerns to patient safety within the acute hospital environment, equating to 1700 falls per year in an 800-bed general hospital. …”
Get full text

Fall risk screening: Audiologists’ perceived knowledge, views and reported practice by Kayla J. McFarlane, Amisha Kanji, Alida Naude

“…Background: Falls among older adults are a major public health issue. …”
Get full text

BedEye: A Bed Exit and Bedside Fall Warning System Based on Skeleton Recognition Technology for Elderly Patients by Liang-Bi Chen, Wan-Jung Chang, Tzu-Chin Yang

“…Falls are an important medical safety issue, and patients older than 65 years are the most prone to falling in hospitals. …”
Get full text

Energy-Efficient Fall-Detection System Using LoRa and Hybrid Algorithms by Manny Villa, Eduardo Casilari

“…Wearable fall-detection systems have received significant research attention during the last years. …”
Get full text

Exploring the organisation and delivery of falls management in care homes for older people in England by K. Robinson, P. L. Logan, A. L. Gordon, S. Timmons, T. Masud, L. Rees, A. Kilby, W. Lawry, F. Hallam-Bowles

Get full text

Flexible and Stable GaN Piezoelectric Sensor for Motion Monitoring and Fall Warning by Zhiling Chen, Kun Lv, Renqiang Zhao, Yaxian Lu, Ping Chen

“…In addition, an intelligent fall warning system was proposed for the personalized healthcare application of elders by applying machine learning to analyze data collected from typical activities. …”
Get full text

Reducing inpatient falls: a quality improvement initiative in a medical ward by Talha Arfan Butt, Rana Areez Ahmed Khan

“…Compliance audits and post-fall huddles were implemented to identify learning opportunities.5Cycle 3: sustainability measures were embedded, including leadership support, real-time feedback and continuous monitoring through digital dashboards.6 Results and discussion: Data were collected prospectively, and fall rates were measured per 1,000 patient bed-days. …”
Get full text

A Novel Cooperative AI-Based Fall Risk Prediction Model for Older Adults by Deepika Mohan, Peter Han Joo Chong, Jairo Gutierrez

“…Artificial intelligence (AI) and machine learning (ML) offer promising solutions for early fall prediction and continuous health monitoring. …”
Get full text

A Review on Fall Prediction and Prevention System for Personal Devices: Evaluation and Experimental Results by Masoud Hemmatpour, Renato Ferrero, Bartolomeo Montrucchio, Maurizio Rebaudengo

“…Kinematic features obtained from the data collected from accelerometer and gyroscope have been evaluated in combination with different machine learning algorithms. An experimental analysis compares the evaluated approaches by evaluating their accuracy and ability to predict and prevent a fall. …”
Get full text

Evaluation of convolutional neural networks for the classification of falls from heterogeneous thermal vision sensors by Miguel Ángel López-Medina, Macarena Espinilla, Chris Nugent, Javier Medina Quero

“…The automatic detection of falls within environments where sensors are deployed has attracted considerable research interest due to the prevalence and impact of falling people, especially the elderly. …”
Get full text

Fall Detection in Q-eBall: Enhancing Gameplay Through Sensor-Based Solutions by Zeyad T. Aklah, Hussein T. Hassan, Amean Al-Safi, Khalid Aljabery

“…Offline experiments were conducted to assess the performance of four machine learning models, which were K-Nearest Neighbors (KNNs), Support Vector Machine (SVM), Random Forest (RF), and Long Short-Term Memory (LSTM), for falls detection. …”
Get full text

Thermal and RGB image dataset for detection and management of Fall Army Worm (FAW) infestation in maize by Prakash Sandhya, B Venkataramana, T. Pradeesh Kumar, Radhakrishnan Sujatha

Get full text

Iterative Design of a Decision Support System for Fall Risk Detection in residential care facilities by Tschorn Niklas, Keuchel Maren, Müller Inga, Milkov Sarah, Potthoff Christian, Windrath Dioselina, Weber Yvonne, Meister Sven, Burmann Anja

“…As falls are one of the most common health problems in care facilities, we will present a concept of a DSS for the prevention of falls that was developed in the PFLIP research project. …”
Get full text

Vision-Based Fall Risk Assessment Through Attention Augmented Neural Encoding and Data Augmentation by Chunhua Pan, Rui Miao, Qing Zhang, Boting Qu, Xin Wang

“…To address these limitations, we propose GTAE-FRA, a novel vision-based deep learning framework for fall risk assessment that leverages standard, widely available RGB cameras to record Five times Sit-To-Stand (FSTS) test videos. …”
Get full text

See no evil, hear no evil: the rise and fall of child sexual abuse in the 20th century by Steven J Collings

Get full text

Intervention to systematize fall risk assessment and prevention in older hospitalized adults: a mixed methods study by Johann Stuby, Pascal Leist, Noël Hauri, Sanjana Jeevanji, Marie Méan, Carole E. Aubert

“…Abstract Background Fall-prevention interventions are efficient but resource-requiring and should target persons at higher risk of falls. …”
Get full text

Neural Network Approach for Fatigue Crack Prediction in Asphalt Pavements Using Falling Weight Deflectometer Data by Bishal Karki, Sayla Prova, Mayzan Isied, Mena Souliman

“…The model is calibrated utilizing Falling Weight Deflectometer (FWD) testing data, alongside essential pavement characteristics such as layer thickness, air void percentage, asphalt binder proportion, traffic loads (Equivalent Single Axle Loads or ESALs), and mean annual temperature. …”
Get full text