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Energy-saving of Wearable Devices. Presented by Zhuo Li. Energy-saving of Wearable Devices Introduction What is wearable devices Motivation Ralated work System Algorithm Furture work Improvement New directions. Energy-saving of Wearable Devices Introduction
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Energy-saving of Wearable Devices Presented by Zhuo Li
Energy-saving of Wearable Devices • Introduction • What is wearable devices • Motivation • Ralated work • System • Algorithm • Furture work • Improvement • New directions
Energy-saving of Wearable Devices • Introduction • What is wearable devices • Motivation • Ralated work • System • Algorithm • Furture work • Improvement • New directions
motivation Wearable computing, as originally presented by Steve Mann in 1996, emphasized a shift in computing paradigm. Computer would no longer be separated,but an unobtrusive extension.
motivation • Nowadays,we do not have self-built multi-sensors wearable computing system, which can save energy in many areas. • We want to build one that have following advantages: • 1. Using it, we can monitor the periodical behavior of big wild animals, • no matter how big itis. • 2. Different combination of classification among single sensors means different pattern. However, some classification in combination is redundant, andless sensor’s classification can represent the whole pattern. • 3. Based on the former principle,we save the energy as much as possible.
Energy-saving of Wearable Devices • Introduction • What is wearable devices • Motivation • Ralated work • System • Algorithm • Furture work • Improvement • New directions
system MPU-6050+Arduino pro mini+Wifi Module
system Feature Selection sensors Segmentation Classification Segmentationis intended to identify ‘start’ and ‘end’ points. Feature selection isresponsible for calculatingstatistical and morphologicalcharacteristics of the signal segment. Classificationalgorithm is utilized to determine the current state of theuser.
Algorithm Relavance Analysis Redundancy Analysis Redundancy Graph Min cost system Relavance Analysis&Redundancy Analysis:To determine the relationship between different features and the features in each sensors' relationship. Redundancy Graph:Make the relationship of different sensors clearly and calculate which sensor should be off. Min cost system:Based on the primor algorithm,
Algorithm Defination 1:Symmetric Uncertainly The symmetric uncertaintybetween two discrete random variables X and Y is givenby: The symmetric uncertainty is actually the normalizedinformation gain and is always between 0 and 1, where U =1 means that knowing the value of either variable cancompletely predict the other variable, and U =0 indicatesthat the two variables are completely independent.
Algorithm Defination 2:Irrelated and Strong related feature Given an exhaustive set of nfeatures F={f1, f2, . . .; fn} and a set of human actions A ={a1, a2, . . . , ah} to be classified, a feature fi is irrelevant to theclassification task if Otherwise the feature is strong related. Then we put all the irrelated featrue in the Necessary Graph. The left featrues are in the Redundancy Graph.
Algorithm G =(V, E, W) is called redundancy graph,where V is a set of m vertices, V = {u1, u2, . . . , um} associatedwith the m relevant features, E = {e1, e2, . . . , er} is the set of rfeature pairs that are strongly correlated, and W = {w1, w2,. . .; wm} is the set of weights, assigned to the vertices, denotingthe computing cost and energy consumption associated with each feature.
Algorithm Let all the weights be equal to one unit, that is W ={w1,w2, w3, w4, w5} ={1, 1, 1, 1, 1}. In this case, we treats allfeatures equally and thus, the optimal feature set consists oftwo vertices, specifically f1 and f3. However, if we modifythe weight set to W={10, 1, 1, 1, 1}, we gives more considerationto vertices with lower weights and accordingly,features f4 and f5 will be favored over f1. In the recentscenario the optimal feature set will contain three vertices, i.e.,f4, f5, and f3. As such, the computation energy cost will bedecreased from 11 to 3 units.
Right arm: Left arm: Left leg: Left leg:
Energy-saving of Wearable Devices • Introduction • What is wearable devices • Motivation • Ralated work • System • Algorithm • Furture work • Improvement • New directions
Improvement • The arduino only has a SRAM of 32kb,and the protocols of communication almost cost all. • Try to cut down the space the protocols occupied,and then realize the feature selection on the arduino. • Finish the task on the mobilephone and send signals back to the arduino to make the helpless sensors off. • Find a more compelling ways to analysis the relation between different sensors. • Calculate the approximate energy the system can saving.
New directions Location change of sensor: Some sensor’s location maybe notstable. If the location changed, the central processor must know it and deal with it. In another word, improve the robustness of current work. Nonperiodic action:Our system are based on the periodic action,however most of the actions are nonperiodic.Try to find new ways to decide how to deal with the nonperiodic action.
