Energy-Efficient Accelerometer Data Transfer for Human Body Movement Studies

2010 ◽  
Author(s):  
Sungwon Yang ◽  
Mario Gerla
Keyword(s):  
Human Body ◽  
Energy Efficient ◽  
Data Transfer ◽  
Body Movement ◽  
Accelerometer Data

scholarly journals THERMAL AWARE LINK ENERGY EFFICIENT SCHEME FOR BODY AREA NETWORKS

2020 ◽  
Vol 15 (12) ◽  
Author(s):  
M. Javed
Keyword(s):  
Human Body ◽  
Energy Efficient ◽  
Data Transfer ◽  
Body Area Networks ◽  
Residual Energy ◽  
The Body ◽  
Body Area ◽  
Body Tissues ◽  
Daunting Task ◽  
Efficient Scheme

A daunting task in Wireless Body Area Networks (WBANs) is still to develop Effective routing techniques. Small-sized nodes are installed on or within the human body to monitor human health conditions which then deliver the data to servers for analysis. During sensing and data transfer, biomedical sensors work continuously and the temperature of the nodes may rise beyond the threshold limit. This temperature rise may damage the human body tissues as well as the routing mechanism in terms of path losses. To keep the temperature at its normal working value, a priority-based selection of routes is required to prevent data loss during transmission. This will ensure safe and accurate data delivery at the destination. A protocol called “Thermal Aware Link Energy Efficient Scheme for WBANs” (TALEEBA) for workers is proposed to monitor the health of workers in factories. One of the four sinks will collect the data of the nearest worker in the field. As the body temperature of any worker is detected to rise, an alarm will be generated and the supervisor of the workplace will ask the worker to be replaced by some other worker. The same mechanism will continue till the task ends. Our proposed TALEEBA (Thermal Aware Link Energy Efficient Scheme for WBANs) scheme is aligned with current LAEEBA and THE-FAME WBAN schemes. In simulations, we analyze our protocol in terms of stability period, network lifetime, residual energy, a packet sent, packet dropped, and throughput. Hence, the results show stability and network life 50%, a packet sent 20% and throughput 23% are improved in comparison with LABEEA and THE-FAME protocols.

scholarly journals Feasibility of Using Floor Vibration to Detect Human Falls

2020 ◽  
Vol 18 (1) ◽  
pp. 200
Author(s):  
Yu Shao ◽  
Xinyue Wang ◽  
Wenjie Song ◽  
Sobia Ilyas ◽  
Haibo Guo ◽  
...  
Keyword(s):  
Machine Learning ◽  
Human Body ◽  
Nearest Neighbor ◽  
Body Movement ◽  
Modern Society ◽  
Recognition System ◽  
Machine Learning Algorithms ◽  
K Nearest Neighbor ◽  
Study Results ◽  
Floor Vibration

With the increasing aging population in modern society, falls as well as fall-induced injuries in elderly people become one of the major public health problems. This study proposes a classification framework that uses floor vibrations to detect fall events as well as distinguish different fall postures. A scaled 3D-printed model with twelve fully adjustable joints that can simulate human body movement was built to generate human fall data. The mass proportion of a human body takes was carefully studied and was reflected in the model. Object drops, human falling tests were carried out and the vibration signature generated in the floor was recorded for analyses. Machine learning algorithms including K-means algorithm and K nearest neighbor algorithm were introduced in the classification process. Three classifiers (human walking versus human fall, human fall versus object drop, human falls from different postures) were developed in this study. Results showed that the three proposed classifiers can achieve the accuracy of 100, 85, and 91%. This paper developed a framework of using floor vibration to build the pattern recognition system in detecting human falls based on a machine learning approach.

scholarly journals Dual-shot dynamics and ultimate frequency of all-optical magnetic recording on GdFeCo

2021 ◽  
Vol 10 (1) ◽  
Author(s):  
Sicong Wang ◽  
Chen Wei ◽  
Yuanhua Feng ◽  
Hongkun Cao ◽  
Wenzhe Li ◽  
...  
Keyword(s):  
Magnetization Reversal ◽  
Energy Efficient ◽  
High Speed ◽  
Data Transfer ◽  
Laser Pulses ◽  
Time Resolved ◽  
All Optical ◽  
Fs Laser ◽  
High Speed Data ◽  
Natural Way

AbstractAlthough photonics presents the fastest and most energy-efficient method of data transfer, magnetism still offers the cheapest and most natural way to store data. The ultrafast and energy-efficient optical control of magnetism is presently a missing technological link that prevents us from reaching the next evolution in information processing. The discovery of all-optical magnetization reversal in GdFeCo with the help of 100 fs laser pulses has further aroused intense interest in this compelling problem. Although the applicability of this approach to high-speed data processing depends vitally on the maximum repetition rate of the switching, the latter remains virtually unknown. Here we experimentally unveil the ultimate frequency of repetitive all-optical magnetization reversal through time-resolved studies of the dual-shot magnetization dynamics in Gd27Fe63.87Co9.13. Varying the intensities of the shots and the shot-to-shot separation, we reveal the conditions for ultrafast writing and the fastest possible restoration of magnetic bits. It is shown that although magnetic writing launched by the first shot is completed after 100 ps, a reliable rewriting of the bit by the second shot requires separating the shots by at least 300 ps. Using two shots partially overlapping in space and minimally separated by 300 ps, we demonstrate an approach for GHz magnetic writing that can be scaled down to sizes below the diffraction limit.

scholarly journals A Quality-Configurable Approximate Serial Bus for Energy-Efficient Sensory Data Transfer

2018 ◽  
Vol 8 (3) ◽  
pp. 379-390 ◽  
Author(s):  
Setareh Behroozi ◽  
Vijay Raghunathan ◽  
Anand Raghunathan ◽  
Younghyun Kim
Keyword(s):  
Energy Efficient ◽  
Data Transfer ◽  
Sensory Data

scholarly journals Rebar Worker Analysis Using a 3-Axis Accelerometer

2021 ◽  
Vol 7 ◽  
Author(s):  
Takuma Akaki ◽  
Tomoyuki Gondo
Keyword(s):  
Body Movement ◽  
Building Construction ◽  
Accelerometer Data ◽  
Construction Site ◽  
Work Intensity ◽  
Construction Sites ◽  
Repetitive Tasks ◽  
The Difference ◽  
Total Body ◽  
Large Building

The purpose of the present study is to grasp the situation of construction sites easily by distinguishing the movements of construction workers at construction sites from the accelerometer data attached to their waists. For the construction manager to accurately perceive the active or inactive state of his workers, their movements were classified into three distinct categories: walking, standing, and sitting. We tracked and observed two rebar workers for 5 days at a large building construction site. Their movements were classified by two-axis plots of (1) the difference between the maximum and minimum absolute values and (2) the value of acceleration at each second, and visualized by a heatmap among others for this trial. The results showed that despite the difficulty in distinguishing rebar work without a total body movement while sitting, the accuracy of discrimination was 60–80% in walking and sitting. From this analysis, we were able to identify repetitive tasks and the differences between morning and afternoon tasks. Furthermore, by applying simple visualization, we could concisely represent changes in work intensity over a relatively long period.

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