Article
Biology
Athanasios Triantafyllou, Georgios Papagiannis, Sophia Stasi, Daphne Bakalidou, Maria Kyriakidou, George Papathanasiou, Elias C. Papadopoulos, Panayiotis J. Papagelopoulos, Panayiotis Koulouvaris
Summary: The recurrence rate after lumbar spine disc surgeries is estimated to be 5-15%. Lumbar spine flexion of more than 10 degrees is mentioned as the most harmful load to the operated disc level that could lead to recurrence during the first six postoperative weeks. This study used wearable sensors technology to quantify flexions during daily living following such surgeries for six weeks postoperatively. The results showed that patients had a 30% normal lumbar motion after the first postoperative week, which increased to almost 75% at the end of the sixth week.
Article
Chemistry, Analytical
Paolo Bellitti, Michela Borghetti, Nicola Francesco Lopomo, Emilio Sardini, Mauro Serpelloni
Summary: More than 500,000 injuries involving the anterior cruciate ligament (ACL) are diagnosed in Europe every year. To assess ACL injuries quantitatively, a smart knee brace equipped with strain sensors and inertial measurement units (IMUs) was developed. Preliminary findings suggest that the smart brace can effectively discriminate possible ACL lesions.
Article
Chemistry, Analytical
Samer A. A. Mohamed, Uriel Martinez-Hernandez
Summary: This paper proposes a light-weight architecture for activity recognition using wearable sensors. Time-domain features of the sensors data are extracted systematically, and a small high-speed artificial neural network is used for activity recognition. The experiments demonstrate that the proposed architecture can achieve accurate and fast activity recognition with reduced computational complexity.
Article
Engineering, Electrical & Electronic
Majd Saleh, Manuel Abbas, Regine Le Bouquin Jeannes
Summary: This article discusses the limitations of wearable fall detection systems, focusing on issues related to datasets and sensors, and proposes a comprehensive data acquisition system. The FallAllD dataset collects a large amount of data and can effectively evaluate the performance of deep learning and classical algorithms.
IEEE SENSORS JOURNAL
(2021)
Article
Computer Science, Information Systems
Martin Gjoreski, Bhargavi Mahesh, Tine Kolenik, Jens Uwe-Garbas, Dominik Seuss, Hristijan Gjoreski, Mitja Lustrek, Matjaz Gams, Veljko Pejovic
Summary: This study investigated and evaluated methods for cognitive load classification using wearable sensor data, finding that using multimodal sensor data and classical classification algorithms can lead to more robust and accurate models. Individual differences and task difficulty also have a significant impact on classification performance.
Review
Engineering, Industrial
Nathan A. Edwards, Maria K. Talarico, Ajit Chaudhari, Cody J. Mansfield, James Onate
Summary: This systematic review examines the use of accelerometers and inertial measurement units in quantifying movement patterns of tactical athletes. The study found that accelerometers were the most commonly used sensor type, and physical activity was the primary outcome variable. However, research on firefighters, emergency medical services, and law enforcement officers was limited. Future research should focus on making quantified movement data more accessible and user-friendly for non-research personnel.
APPLIED ERGONOMICS
(2023)
Article
Chemistry, Analytical
Angela R. Weston, Brian J. Loyd, Carolyn Taylor, Carrie Hoppes, Leland E. Dibble
Summary: This study aimed to determine the ability of wearable sensors and data processing algorithms to discern motion restrictions during daily living activities. The results showed that wearable sensors accurately captured significant differences in head and trunk kinematics, including rotational velocity, amplitude, and head-trunk coupling, between restricted and non-restricted conditions. These findings support the ecological validity of using wearable sensors to quantify movement alterations during real-world scenarios.
Article
Computer Science, Artificial Intelligence
Atul Chaudhary, Hari Prabhat Gupta, K. K. Shukla
Summary: This paper proposes an online system that recognizes activities of daily living (ADL) in real-time, considering the long-tailed class distribution. The system generates features using conventional and deep learning, and utilizes ensemble technique for feature concatenation. It minimizes a loss function that addresses the class imbalance problem and enhances the discriminative power of deep learning features.
IEEE TRANSACTIONS ON EMERGING TOPICS IN COMPUTATIONAL INTELLIGENCE
(2022)
Article
Orthopedics
Kyohei Nishida, Caiqi Xu, Tom Gale, William Anderst, Freddie Fu
Summary: This study found that women had greater knee abduction angle and ACL relative elongation than men during various activities, making them more susceptible to ACL injury. The differences in kinematics were minimal, while the differences in ACL relative elongation were relatively stable. These results provide valuable information for identifying abnormal knee kinematics and ACL elongation in athletes after ACL injury.
JOURNAL OF ORTHOPAEDIC RESEARCH
(2022)
Article
Engineering, Electrical & Electronic
Erhan Kavuncuoglu, Esma Uzunhisarcikli, Billur Barshan, Ahmet Turan Ozdemir
Summary: With sensor-based wearable technologies, the real-time and high precision monitoring and recognition of human physical activities is crucial for supporting the daily living needs of elderly individuals. Through the investigation of different sensor combinations and machine learning algorithms, it was found that using SVM and M.k-NN classifiers with the AM sensor type combination yielded the highest classification accuracy rates in fall and multi-class activity recognition. The dataset used in this study has also been publicly made available to facilitate future research.
DIGITAL SIGNAL PROCESSING
(2022)
Article
Biology
Robert Rockenfeller
Summary: This study aims to investigate the changes in spinal shape during daily activities and identify which parts undergo significant changes. Motion capture data from 17 healthy individuals were used to analyze spinal shape characteristics. The results show that individual spinal shape is recognizably preserved during all activities, with walking having no significant effect on spinal curvature, while sitting and forward bending significantly altering the lumbar and whole spine curvature, respectively. Torsion did not show any significant alterations.
COMPUTERS IN BIOLOGY AND MEDICINE
(2023)
Review
Environmental Sciences
Nicola Camp, Martin Lewis, Kirsty Hunter, Julie Johnston, Massimiliano Zecca, Alessandro Di Nuovo, Daniele Magistro
Summary: The use of technology to monitor ADL of older adults is increasingly common, but there is significant variation in the recognition of ADL, how ADL are defined, and the types of technology utilized in monitoring systems.
INTERNATIONAL JOURNAL OF ENVIRONMENTAL RESEARCH AND PUBLIC HEALTH
(2021)
Article
Physiology
Gaeelle Prigent, Salil Apte, Anisoara Paraschiv-Ionescu, Cyril Besson, Vincent Gremeaux, Kamiar Aminian
Summary: Understanding the influence of running-induced acute fatigue on the body's homeostasis is crucial for optimizing training outcomes. This study examined the evolution of biomechanical, physiological, and psychological facets during a half-marathon race and found significant alterations in gait, heart rate, and perceived fatigue. Faster runners demonstrated better physiological judgment and greater sensitivity to neuromuscular changes in running gait.
FRONTIERS IN PHYSIOLOGY
(2022)
Article
Engineering, Biomedical
Jeonghoon Oh, Zachary Ripic, Joseph F. Signorile, Michael S. Andersen, Christopher Kuenze, Michael Letter, Thomas M. Best, Moataz Eltoukhy
Summary: This study compared lower extremity kinetics during over-ground gait and stair ascent between ACLR affected limbs, unaffected limbs, and healthy control subjects using a portable low-cost motion capture method. The findings suggested no significant differences during over-ground gait, but compensatory strategies were observed during stair ascent in the ACLR limbs.
MEDICAL ENGINEERING & PHYSICS
(2022)
Review
Computer Science, Information Systems
Nian Chi Tay, Tee Connie, Thian Song Ong, Andrew Beng Jin Teoh, Pin Shen Teh
Summary: This study compares and contrasts the formation of abnormal behavior detection (ABD) systems in activities of daily living (ADL) using different input data types and modeling techniques. The study also addresses the lack of datasets in ABD in ADL and aims to guide new researchers in understanding the field and serve as a reference for future study in smart homes.
