scholarly journals Human Falling Recognition Based on Movement Energy Expenditure Feature

2021 ◽  
Vol 2021 ◽  
pp. 1-12
Author(s):  
Daohua Pan ◽  
Hongwei Liu
Keyword(s):  
Energy Expenditure ◽  
Recognition Accuracy ◽  
Human Movement ◽  
The Elderly ◽  
Detection Algorithm ◽  
Body Tilt ◽  
Behavior Recognition ◽  
Daily Lives ◽  
Threshold Detection ◽  
New Feature

Falls in the elderly are a common phenomenon in daily life, which causes serious injuries and even death. Human activity recognition methods with wearable sensor signals as input have been proposed to improve the accuracy and automation of daily falling recognition. In order not to affect the normal life behavior of the elderly, to make full use of the functions provided by the smartphone, to reduce the inconvenience caused by wearing sensor devices, and to reduce the cost of monitoring systems, the accelerometer and gyroscope integrated inside the smartphone are employed to collect the behavioral data of the elderly in their daily lives, and the threshold analysis method is used to study the human falling behavior recognition. Based on this, a three-level threshold detection algorithm for human fall behavior recognition is proposed by introducing human movement energy expenditure as a new feature. The algorithm integrates the changes of human movement energy expenditure, combined acceleration, and body tilt angle in the process of falling, which alleviates the problem of misjudgment caused by using only the threshold information of acceleration or (and) angle change to discriminate falls and improves the recognition accuracy. The recognition accuracy of this algorithm is verified by experiments to reach 95.42%. The APP is also devised to realize the timely detection of fall behavior and send alarms automatically.

The Effect of Sensor Placement and Number on Physical Activity Recognition and Energy Expenditure Estimation (Preprint)

2020 ◽  
Author(s):  
Anis Davoudi ◽  
Mamoun T. Mardini ◽  
Dave Nelson ◽  
Fahd Albinali ◽  
Sanjay Ranka ◽  
...  
Keyword(s):  
Physical Activity ◽  
Older Adults ◽  
Energy Expenditure ◽  
Activity Recognition ◽  
Recognition Accuracy ◽  
Body Position ◽  
Computer Work ◽  
Accelerometer Data ◽  
Upper Arm ◽  
Lifestyle Activities

BACKGROUND Research shows the feasibility of human activity recognition using Wearable accelerometer devices. Different studies have used varying number and placement for data collection using the sensors. OBJECTIVE To compare accuracy performance between multiple and variable placement of accelerometer devices in categorizing the type of physical activity and corresponding energy expenditure in older adults. METHODS Participants (n=93, 72.2±7.1 yrs) completed a total of 32 activities of daily life in a laboratory setting. Activities were classified as sedentary vs. non-sedentary, locomotion vs. non-locomotion, and lifestyle vs. non-lifestyle activities (e.g. leisure walk vs. computer work). A portable metabolic unit was worn during each activity to measure metabolic equivalents (METs). Accelerometers were placed on five different body positions: wrist, hip, ankle, upper arm, and thigh. Accelerometer data from each body position and combinations of positions were used in developing Random Forest models to assess activity category recognition accuracy and MET estimation. RESULTS Model performance for both MET estimation and activity category recognition strengthened with additional accelerometer devices. However, a single accelerometer on the ankle, upper arm, hip, thigh, or wrist had only a 0.03 to 0.09 MET increase in prediction error as compared to wearing all five devices. Balanced accuracy showed similar trends with slight decreases in balanced accuracy for detection of locomotion (0-0.01 METs), sedentary (0.13-0.05 METs) and lifestyle activities (0.08-0.04 METs) compared to all five placements. The accuracy of recognizing activity categories increased with additional placements (0.15-0.29). Notably, the hip was the best single body position for MET estimation and activity category recognition. CONCLUSIONS Additional accelerometer devices only slightly enhance activity recognition accuracy and MET estimation in older adults. However, given the extra burden of wearing additional devices, single accelerometers with appropriate placement appear to be sufficient for estimating energy expenditure and activity category recognition in older adults.

scholarly journals A New Real-Time Pinch Detection Algorithm Based on Model Reference Kalman Prediction and SRMS for Electric Adjustable Desk

Sensors ◽  
2020 ◽  
Vol 20 (17) ◽  
pp. 4699
Author(s):  
Minming Gu ◽  
Yajie Wei ◽  
Haipeng Pan ◽  
Yujia Ying
Keyword(s):  
Pulse Width Modulation ◽  
Detection Algorithm ◽  
Prediction Algorithm ◽  
Actual System ◽  
Model Reference ◽  
Threshold Detection ◽  
System A ◽  
Working Principle ◽  
Current Sampling ◽  
Kalman Prediction

This paper presents a new algorithm based on model reference Kalman torque prediction algorithm combined with the sliding root mean square (SRMS). It is necessary to improve the accuracy and reliability of the pinch detection for avoiding collision with the height adjustable desk and accidents on users. Motors need to regulate their position and speed during the operation using different voltage by PWM (Pulse Width Modulation) to meet the requirement of position synchronization. It causes much noise and coupling information in the current sampling signal. Firstly, to analyze the working principle of an electric height adjustable desk control system, a system model is established with consideration of the DC (Direct Current) motor characteristics and the coupling of the system. Secondly, to precisely identify the load situation, a new model reference Kalman perdition method is proposed. The load torque signal is selected as a pinch state variable of the filter by comparing the current signal. Thirdly, to meet the need of the different loads of the electric table, the sliding root means square (SRMS) of the torque is proposed to be the criterion for threshold detection. Finally, to verify the effectiveness of the algorithm, the experiments are carried out in the actual system. Experimental results show that the algorithm proposed in this paper can detect the pinched state accurately under different load conditions.

scholarly journals Functional capability and violence situations against the elderly

2014 ◽  
Vol 27 (5) ◽  
pp. 392-398 ◽  
Author(s):  
Andréa Mathes Faustino ◽  
Lenora Gandolfi ◽  
Leides Barroso de Azevedo Moura
Keyword(s):  
Activities Of Daily Living ◽  
Daily Living ◽  
Physical Violence ◽  
The Elderly ◽  
Population Based ◽  
Cross Sectional Study ◽  
Functional Capability ◽  
Cross Sectional ◽  
Daily Lives ◽  
Partial Dependence

Objective To verify whether there is a connection between the functional capacity of the elderly and the presence of violent situations in their daily lives. Methods A population-based cross-sectional study developed with 237 elderly individuals. Standard and validated research instruments were used. Results Mean age of 70.25 years (standard deviation of 6.94), 69% were female, 76% were independent in basic activities of daily living and 54% had a partial dependence on at least one instrumental activity. The most prevalent violence was psychological and the relation between being dependent on basic activities of daily living and suffering physical violence was statistically significant. Conclusion When the elderly needs assistance to perform self-care activities, there is a greater chance of exposure to a situation of abuse, such as physical violence.

ANALYSIS OF THE EFFECTS OF SPINE STABILIZATION EXERCISES USING A WHOLE BODY TILT DEVICE ON MUSCLE FORCES IN THE SPINE

2014 ◽  
Vol 14 (06) ◽  
pp. 1440003
Author(s):  
KAP-SOO HAN ◽  
CHANG HO YU ◽  
MYOUNG-HWAN KO ◽  
TAE KYU KWON
Keyword(s):  
Inverse Dynamics ◽  
The Elderly ◽  
Body Tilt ◽  
Whole Body ◽  
Muscle Forces ◽  
Activation Patterns ◽  
Muscle Strengthening ◽  
Spine Stabilization ◽  
Inverse Dynamics Analysis ◽  
The Right

The objective of the study was to investigate the effects of 3D stabilization exercises using a whole body tilt device on forces in the trunk, such as individual muscle forces and activation patterns, maximum muscle activities and spine loads. For this sake, a musculoskeletal (MS) model of the whole body was developed, and an inverse dynamics analysis was performed to predict the forces on the spine. An EMG measurement experiment was conducted to validate the muscle forces and activation patterns. The MS model was rotated and tilted in eight different directions: anterior (A), posterior (P), anterior right (AR), posterior right (PR), anterior left (AL), posterior left (PL), right (R) and left (L), replicating the directions of the 3D spine balance exercise device, as performed in the experiment. The anterior directions of the tilt primarily induced the activation of long and superficial back muscles and the posterior directions activated the front muscles. However, deep muscles, such as short muscles and multifidi, were activated in all directions of the tilt. The resultant joint forces in the right and left directions of the tilt were the least among the directions, but higher muscle activations and more diverse muscle recruitments than other positions were observed. Therefore, these directions of tilt may be suitable for the elderly and rehabilitation patients who require muscle strengthening with less spinal loads. In the present investigation, it was shown that 3D stabilization exercises could provide considerable muscle exercise effects with a minimum perturbation of structure. The results of this study can be used to provide safety guidelines for muscle exercises using this type of tilting device. Therefore, the proposed direction of tilt can be used to strengthen targeted muscles, depending on the patients' muscular condition.

scholarly journals Effect of Eight Weeks Water Resistance Training with Dark Chocolate Supplementation on the Balance of the Elderly

2020 ◽  
Author(s):  
Maryam Mousavi ◽  
Farshad Ghazalian
Keyword(s):  
Resistance Training ◽  
Water Resistance ◽  
Training Group ◽  
The Elderly ◽  
Dark Chocolate ◽  
Control Group ◽  
Daily Lives ◽  
Significant Difference ◽  
Group Water ◽  
Age Range

Introduction: Improving balance in the daily lives of the elderly plays an important role, especially in reducing their risk of falling. Therefore, the aim of the present study was to investigate the effect of eight weeks water resistance training with dark chocolate supplementation on the balance of the elderly.Methods: In this study, 38 elderly people with an age range of 73-60 years were randomly divided into four groups. Participants in groups, included water resistance training and water resistance training + dark chocolate, performed water resistance training for eight weeks. In this period, groups of dark chocolate and water resistance training + dark chocolate, consumed 6 pieces of dark chocolate 83%, 5 gr per day, and the control group did not have any training or supplementation. The stork test (flamingo) was used to assess balance. The results of covariance analysis (ANOVA) showed that there was a statistically significant difference between the four groups. Data were evaluated using Excel and SPSS-25 (p≤ 0.05). Results: The adjusted means after eliminating the effect of pre-test scores showed that the water resistance-training group had a higher mean than the other three groups and the weakest scores belonged to the control group. The results of other groups were as followed: experimental group 18.77, chocolate group 16.88, combined group (water resistance training + dark chocolate) 17.24 and control group 9.77. The results of Benferoni test showed that there was a significant difference between the water resistance training group and the control group (p = 0.006). Conclusion: According to the results of this study, it seems that eight weeks of water resistance training and dark chocolate supplementation can improve the balance and quality of life of the elderly.

scholarly journals Implementation and Application of Fall Detection on Mobile Perception Robot

2021 ◽  
Vol 2136 (1) ◽  
pp. 012053
Author(s):  
Zeyu Chen
Keyword(s):  
Elderly Population ◽  
Fall Detection ◽  
The Elderly ◽  
Detection Algorithm ◽  
Data Sets ◽  
Construction Technology ◽  
Robot Operating System ◽  
Map Construction ◽  
Robot Perception ◽  
Window Method

Abstract With the rapid increase in the number of people living in the elderly population, reducing and dealing with the problem of falls in the elderly has become the focus of research for decades. It is impossible to completely eliminate falls in daily life and activities. Detecting a fall in time can protect the elderly from injury as much as possible. This article uses the Turtlebot robot and the ROS robot operating system, combined with simultaneous positioning and map construction technology, Monte Carlo positioning, A* path planning, dynamic window method, and indoor map navigation. The YOLO network is trained using the stance and fall data sets, and the YOLOv4 target detection algorithm is combined with the robot perception algorithm to finally achieve fall detection on the turtlebot robot, and use the average precision, precision, recall and other indicators to measure.

Nutrition: Macronutrient metabolism

2020 ◽  
pp. 1839-1854
Author(s):  
Keith N. Frayn ◽  
Rhys D. Evans
Keyword(s):  
Amino Acids ◽  
Food Intake ◽  
Energy Expenditure ◽  
Complex Systems ◽  
Metabolic Control ◽  
Energy Intake ◽  
Energy Supply ◽  
The Body ◽  
Daily Lives ◽  
Storage Pools

Food intake is sporadic and, in many cultures, occurs in three daily boluses. At the same time, energy expenditure is continuous and can vary to a large extent independently of the pattern of energy intake, although fixed or predictable demands (e.g. through occupation) means that in most persons food intake and energy expenditure are soon balanced. The body has developed complex systems that direct excess nutrients into storage pools; as they are needed, they also regulate the mobilization of nutrients from these pools. Carbohydrate, lipid, and protein (the latter a source of amino acids) are the three types of energy supply that are stored variably and assimilated from food each day. That we can carry on our daily lives without thinking about whether to store or mobilize fuels, and which to use, attests to the remarkable efficiency and refinement of these systems of metabolic control.

scholarly journals A Fast Indoor/Outdoor Transition Detection Algorithm Based on Machine Learning

Sensors ◽  
2019 ◽  
Vol 19 (4) ◽  
pp. 786 ◽  
Author(s):  
Yida Zhu ◽  
Haiyong Luo ◽  
Qu Wang ◽  
Fang Zhao ◽  
Bokun Ning ◽  
...  
Keyword(s):  
Recognition Accuracy ◽  
Detection Algorithm ◽  
Location Based Services ◽  
Outdoor Environment ◽  
Detection Accuracy ◽  
Sliding Windows ◽  
Switching Delay ◽  
Spatial Geometry ◽  
Gnss Measurements ◽  
Indoor And Outdoor

The widespread popularity of smartphones makes it possible to provide Location-Based Services (LBS) in a variety of complex scenarios. The location and contextual status, especially the Indoor/Outdoor switching, provides a direct indicator for seamless indoor and outdoor positioning and navigation. It is challenging to quickly detect indoor and outdoor transitions with high confidence due to a variety of signal variations in complex scenarios and the similarity of indoor and outdoor signal sources in the IO transition regions. In this paper, we consider the challenge of switching quickly in IO transition regions with high detection accuracy in complex scenarios. Towards this end, we analyze and extract spatial geometry distribution, time sequence and statistical features under different sliding windows from GNSS measurements in Android smartphones and present a novel IO detection method employing an ensemble model based on stacking and filtering the detection result by Hidden Markov Model. We evaluated our algorithm on four datasets. The results showed that our proposed algorithm was capable of identifying IO state with 99.11% accuracy in indoor and outdoor environment where we have collected data and 97.02% accuracy in new indoor and outdoor scenarios. Furthermore, in the scenario of indoor and outdoor transition where we have collected data, the recognition accuracy reaches 94.53% and the probability of switching delay within 3 s exceeds 80%. In the new scenario, the recognition accuracy reaches 92.80% and the probability of switching delay within 4 s exceeds 80%.

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