scholarly journals Development and testing of the aerial porter exoskeleton

2022 ◽  
Vol 3 ◽  
Author(s):  
W. Brandon Martin ◽  
Alexander Boehler ◽  
Kevin W. Hollander ◽  
Darren Kinney ◽  
Joseph K. Hitt ◽  
...  
Keyword(s):  
Back Pain ◽  
Activity Recognition ◽  
Recognition Algorithm ◽  
Workplace Injury ◽  
Back Injuries ◽  
Perceived Effort ◽  
Onboard Computer ◽  
The World ◽  
Lost Productivity ◽  
Powered Exoskeleton

Abstract Back pain is one of the largest drivers of workplace injury and lost productivity in industries around the world. Back injuries were one of the leading reasons in resulting in days away from work at 38.5% across all occupations, increasing for manual laborers to 43%. While the cause of the back pain can vary across occupations, for materiel movers it is often caused from repetitive poor lifting. To reduce the issues, the Aerial Porter Exoskeleton (APEx) was created. The APEx uses a hip-mounted, powered exoskeleton attached to an adjustable vest. An onboard computer calculates the configuration of the user to determine when to activate. Lift form is assisted by using a novel lumbar brace mounted on the sides of the hips. Properly worn, the APEx holds the user upright while providing additional hip torque through a lift. This was tested by having participants complete a lifting test with the exoskeleton worn in the “on” configuration compared with the exoskeleton not worn. The APEx has been shown to deliver 30 Nm of torque in lab testing. The activity recognition algorithm has also been shown to be accurate in 95% of tested conditions. When worn by subjects, testing has shown average peak reductions of 14.9% BPM, 8% in VO2 consumption, and an 8% change in perceived effort favoring the APEx.

EVALUATION OF THE EFECTS OF EMBEDDING THERAPY AND ELECTRONIC ACUPUNCTURE COMBINED WITH “DOC HOAT TANG KY SINH” REMEDY IN THE TREATMENT OF LOW BACK PAIN BY OSTEOARTHRITIS

2016 ◽  
pp. 33-38
Author(s):  
Thi Ngoc Dung Thai ◽  
Thi Tan Nguyen
Keyword(s):  
Low Back Pain ◽  
Back Pain ◽  
Effective Treatment ◽  
Key Words ◽  
Low Back ◽  
Good Level ◽  
Embedding Therapy ◽  
The World ◽  
Group 2 ◽  
Group 1

Background: Low back pain by osteoarthristis is one of the most common diseases in the world as well as in Vietnam, estimated 70-85% people in the world have low back pain sometime in their lives. Obiectives: To evaluate the effects of embedding therapy and electronic acupuncture combined with “Doc hoat tang ky sinh” remedy in the treatment of low back pain by spondylosis. Materials and methods: 72 patients diagnosed of low back pain by spondylosis, were examined and treated at Phu Yen Traditional Medicine Hospital, divided equally into 2 groups (group 1 and group 2). Results: In group 1: Effective treatment at good and fair good level accounted for 41.7% and 41.7%. In group 2: Good level occupied 33.3% and fair good level occupied 55.6%. Conclusion: The ratios of good and fair good in 2 groups were equal (p >0.05) Key words: Low back pain, spondylosis, embedding therapy, electronic acupuncture

Location- and Person-Independent Activity Recognition with WiFi, Deep Neural Networks, and Reinforcement Learning

2021 ◽  
Vol 2 (1) ◽  
pp. 1-25
Author(s):  
Yongsen Ma ◽  
Sheheryar Arshad ◽  
Swetha Muniraju ◽  
Eric Torkildson ◽  
Enrico Rantala ◽  
...  
Keyword(s):  
Neural Network ◽  
Neural Networks ◽  
Reinforcement Learning ◽  
Activity Recognition ◽  
Deep Neural Networks ◽  
State Machine ◽  
Recognition Algorithm ◽  
The State ◽  
Neural Architecture ◽  
Learning Agent

In recent years, Channel State Information (CSI) measured by WiFi is widely used for human activity recognition. In this article, we propose a deep learning design for location- and person-independent activity recognition with WiFi. The proposed design consists of three Deep Neural Networks (DNNs): a 2D Convolutional Neural Network (CNN) as the recognition algorithm, a 1D CNN as the state machine, and a reinforcement learning agent for neural architecture search. The recognition algorithm learns location- and person-independent features from different perspectives of CSI data. The state machine learns temporal dependency information from history classification results. The reinforcement learning agent optimizes the neural architecture of the recognition algorithm using a Recurrent Neural Network (RNN) with Long Short-Term Memory (LSTM). The proposed design is evaluated in a lab environment with different WiFi device locations, antenna orientations, sitting/standing/walking locations/orientations, and multiple persons. The proposed design has 97% average accuracy when testing devices and persons are not seen during training. The proposed design is also evaluated by two public datasets with accuracy of 80% and 83%. The proposed design needs very little human efforts for ground truth labeling, feature engineering, signal processing, and tuning of learning parameters and hyperparameters.

Human interaction recognition method based on parallel multi-feature fusion network

2021 ◽  
Vol 25 (4) ◽  
pp. 809-823
Author(s):  
Qing Ye ◽  
Haoxin Zhong ◽  
Chang Qu ◽  
Yongmei Zhang
Keyword(s):  
Activity Recognition ◽  
Feature Fusion ◽  
Gaussian Model ◽  
Research Direction ◽  
Recognition Algorithm ◽  
Human Interaction ◽  
Amount Of Information ◽  
Interaction Recognition ◽  
Time Periods ◽  
Human Interaction Recognition

Human activity recognition is a key technology in intelligent video surveillance and an important research direction in the field of computer vision. However, the complexity of human interaction features and the differences in motion characteristics at different time periods have always existed. In this paper, a human interaction recognition algorithm based on parallel multi-feature fusion network is proposed. First of all, in view of the different amount of information provided by the different time periods of action, an improved time-phased video down sampling method based on Gaussian model is proposed. Second, the Inception module uses different scale convolution kernels for feature extraction. It can improve network performance and reduce the amount of network parameters at the same time. The ResNet module mitigates degradation problem due to increased depth of neural networks and achieves higher classification accuracy. The amount of information provided in the motion video in different stages of motion time is also different. Therefore, we combine the advantages of the Inception network and ResNet to extract feature information, and then we integrate the extracted features. After the extracted features are merged, the training is continued to realize parallel connection of the multi-feature neural network. In this paper, experiments are carried out on the UT dataset. Compared with the traditional activity recognition algorithm, this method can accomplish the recognition tasks of six kinds of interactive actions in a better way, and its accuracy rate reaches 88.9%.

Sensor positioning for a human activity recognition system using a double layer classifier

2021 ◽  
pp. 095441192110405
Author(s):  
Mohamed H Abdelhafiz ◽  
Mohammed I Awad ◽  
Ahmed Sadek ◽  
Farid Tolbah
Keyword(s):  
Activity Recognition ◽  
Double Layer ◽  
Prediction Accuracy ◽  
Recognition System ◽  
Recognition Algorithm ◽  
Human Gait ◽  
System A ◽  
Election Method ◽  
Single Sensor ◽  
Feature Selector

This paper describes the development of a human gait activity recognition system. A multi-sensor recognition system, which has been developed for this purpose, was reduced to a single sensor-based recognition system. A sensor election method was devised based on the maximum relevance minimum redundancy feature selector to determine the sensor’s optimum position regarding activity recognition. The election method proved that the thigh has the highest contribution to recognize walking, stairs and ramp ascending, and descending activities. A recognition algorithm (which depends mainly on features that are classified by random forest, and selected by a combined feature selector using the maximum relevance minimum redundancy and genetic algorithm) has been modified to compensate the degradation that occurs in the prediction accuracy due to the reduction in the number of sensors. The first modification was implementing a double layer classifier in order to discriminate between the interfered activities. The second modification was adding physical features to the features dictionary used. These modifications succeeded to improve the prediction accuracy to allow a single sensor recognition system to behave in the same manner as a multi-sensor activity recognition system.

scholarly journals The burden of musculoskeletal disorders in the countries of the Eastern Mediterranean region of the World Health Organization (EMRO): Study period 2000-2017

2021 ◽  
Author(s):  
Jafar Yahyavi Dizaj ◽  
Manijeh Soleimanifar ◽  
Reza Hashempour ◽  
Ali Kazemi Karyani ◽  
Fateh Mohsen ◽  
...  
Keyword(s):  
Low Back Pain ◽  
Back Pain ◽  
Neck Pain ◽  
Musculoskeletal Disorders ◽  
Burden Of Disease ◽  
Disease Burden ◽  
World Health ◽  
Low Back ◽  
The World ◽  
Health Organization

Abstract Background: MSK health is imperative for the active participation of an individual in society and MSK related disorders more direly affects a person's quality of life compared other non-communicable disease while it also negatively effects the health system and economy of a country. The current manuscript analyzed and describes the disease burden of MSK disorders in the EMRO region.Methods: This was a cross-sectional descriptive-analytical study conducted based on data published by the Global Burden of Disease Database for MSK disorders up to 2017. The study target comprised population from all countries of the EMRO region of World Health Organization. The present study considered, MSK disorders such as (rheumatoid arthritis), (osteoarthritis), (Low back pain), (neck pain), (gout) and (other Musculoskeletal disorders. The DALY index was used to measure total disease burden.Results: MSK disorders in the world and in the EMRO region was ranked 5th (4% of total disease burden) and 7th (5% of total disease burden) among all diseases in 2017, respectively. Women over 30 years of age in the EMRO region had the highest risk of MSK disorders compared to other regions and in addition, the DALY lost in EMRO region due to MSK disorders was higher in women of all age categories than men. According to the results of this study, Low back pain, Other musculoskeletal disorders and Neck pain had the highest prevalence and burden of disease in the EMRO region and the world. Bahrain, Iran, and Morocco had the highest incidence of MSK DALY score in the EMRO region, and Somalia, Djibouti, and Afghanistan had the lowest incidence of MSK disorders and DALY score, respectively.Conclusion: With the increase in geriatric population and obesity especially in developing countries, consequently, more people tend to suffer from MSK disorders and it is predicted that this spike will continue in the coming decades. Taking in to account the high prevalence and burden of MSK disorders, forces government and health-policy makers to focus more on preventive cares and rehabilitation.

scholarly journals Osteopathic correction in the treatment of pain in the lower back

2020 ◽  
pp. 140-146
Author(s):  
V. O. Belash ◽  
Yu. O. Novikov
Keyword(s):  
Back Pain ◽  
Developed Countries ◽  
Economic Problem ◽  
World Health ◽  
Randomized Controlled ◽  
Lower Back ◽  
The World ◽  
Working Age ◽  
Acute Back Pain ◽  
Health Organization

According to experts of the World Health Organization the lower back pain (LBP) prevalence in developed countries reaches the pandemic size, and it is a serious medical and socio-economic problem. Acute back pain is transformed into chronic in 10–20 % of working age patients′ cases; this causes serious psychological disorders appearing, forms painful behavior and persists even when the initial pain trigger is eliminated. Data from metaanalyses of randomized controlled trials indicate the effectiveness of the osteopathic approach in the treatment of LBP patients. At the same time the osteopathic correction is effective not only for acute pain, but also for chronic pain. A case from clinical practice is described demonstrating the possibility of osteopathic correction of a LBP patient.

scholarly journals Back Pain, Causes, Symptoms and Physiotherapy

2019 ◽  
Vol 3 (3) ◽  
pp. 1-7
Author(s):  
Karski Tomasz
Keyword(s):  
Back Pain ◽  
Lumbar Spine ◽  
Low Temperature ◽  
Brain Dysfunction ◽  
Flexion Contracture ◽  
Minimal Brain Dysfunction ◽  
Spinal Disorders ◽  
Pain Syndromes ◽  
The World ◽  
Lumbar Hyperlordosis

Every fourth woman and every sixth man in the world coming to the Orthopedic or Neurology Departments complain of spinal pains - information from WHO, D ecade of Bones and Joints 2000 - 2010 (Lars Lidgren). According to our observations there are six main causes of such spinal disorders: 1. Lumbar Hyperlordosis causes by flexion contracture of hips and in result anterior tilt of the pelvis. Common in persons with Minimal Brain Dysfunction (MBD). Pain syndromes appear after overstress in some kinds of jobs or in sport. 2. Lumbar or thoracic - lumbar left convex “C” scoliosis in 2nd/A etiopathological group (epg) or ”S” scoliosis in 2nd/B epg in Lublin classification. Pain syndromes appear after overstr ess in some kinds of jobs or in sport. 3. Stiffness of the spine as clinical sign of “I” scoliosis in 3rd epg group in Lublin classification. 4. Spondylolisth esis or spodylolisis in sacral - lumbar or lumbar spine. 5. Urgent “nucleus prolapsed” (in German “Hexen Sch uss”). 6. Extremely cooling of the back part of trunk during work or intensive walking in low temperature. In many of patients in clinical examination we see positive Laseguae test. Sometimes we see weakness of extensors of the feet or paresis of the foot. Our observations confirm that not surgery, but physiotherapy can be beneficial to the patients with spinal problems.

scholarly journals Deep Learning for Activity Recognition in Older People Using a Pocket-Worn Smartphone

Sensors ◽  
2020 ◽  
Vol 20 (24) ◽  
pp. 7195
Author(s):  
Yashi Nan ◽  
Nigel H. Lovell ◽  
Stephen J. Redmond ◽  
Kejia Wang ◽  
Kim Delbaere ◽  
...  
Keyword(s):  
Deep Learning ◽  
Older People ◽  
Activity Recognition ◽  
Short Term Memory ◽  
Good Health ◽  
Recognition Algorithm ◽  
Activity Level ◽  
Free Living ◽  
Acceptable Model ◽  
Long Short Term Memory

Activity recognition can provide useful information about an older individual’s activity level and encourage older people to become more active to live longer in good health. This study aimed to develop an activity recognition algorithm for smartphone accelerometry data of older people. Deep learning algorithms, including convolutional neural network (CNN) and long short-term memory (LSTM), were evaluated in this study. Smartphone accelerometry data of free-living activities, performed by 53 older people (83.8 ± 3.8 years; 38 male) under standardized circumstances, were classified into lying, sitting, standing, transition, walking, walking upstairs, and walking downstairs. A 1D CNN, a multichannel CNN, a CNN-LSTM, and a multichannel CNN-LSTM model were tested. The models were compared on accuracy and computational efficiency. Results show that the multichannel CNN-LSTM model achieved the best classification results, with an 81.1% accuracy and an acceptable model and time complexity. Specifically, the accuracy was 67.0% for lying, 70.7% for sitting, 88.4% for standing, 78.2% for transitions, 88.7% for walking, 65.7% for walking downstairs, and 68.7% for walking upstairs. The findings indicated that the multichannel CNN-LSTM model was feasible for smartphone-based activity recognition in older people.

scholarly journals Low back pain should be considered a health and research priority in Brazil: Lost productivity and healthcare costs between 2012 to 2016

PLoS ONE ◽  
2020 ◽  
Vol 15 (4) ◽  
pp. e0230902 ◽  
Author(s):  
Rodrigo Luiz Carregaro ◽  
Caroline Ribeiro Tottoli ◽  
Daniela da Silva Rodrigues ◽  
Judith E. Bosmans ◽  
Everton Nunes da Silva ◽  
...  
Keyword(s):  
Low Back Pain ◽  
Back Pain ◽  
Healthcare Costs ◽  
Low Back ◽  
Research Priority ◽  
Lost Productivity
Sign in / Sign up

Export Citation Format

Share Document