My Tiny Ping-Pong Helper He Shan ellewood2008@163.com Shanghai JiaoTong University 2015.6.09

Background Desperate need of practice on my own Experience of Wearable computing Why not a ping-pong helper ??

Outline Background Introduction Basic Ideas My Work Result Conclusion Future Work

Outline Background Introduction Basic Ideas My Work Result Conclusion Future Work

Introduction press the button Four basic ways of swing rackets: (1) forehand push (正手推球) (2) backhand push (反手推球) (3) forehand chop (正手搓球) (4) backhand chop (反手搓球) press the button

Introduction Off model On model

Basic Ideas How to determine whether someone have done the right action or not? Using “Activity Recognition Chain (ARC)” ! Procedure: (1) data acquisition and preprocessing (2) data segmentation (3) feature extraction (mean, std, energy, etc) and selection (4) training, classification and identification

My Work 1st step : data acquisition Data Type: Tools: (1) standard data (2) non-standard data Tools: (1) Android smartphone accelerator & gyroscope (2) An app extract data from the phone

My Work Periodic accelerator data Periodic gyroscope data

My Work 2nd step : segmentation Aim: The data segmentation stage identifies those segments of the preprocessed data streams that are likely to contain information about activities (also often referred to as activity detection or “spotting”) Approach: Sliding Window Window moves over the time series data to “extract” data segment Length : 128 & Overlap: 50% data 128 128 … … 128 Reduce the computational load

My Work 3rd step : feature extraction Key: use a minimum number of features that still allow the ARC to achieve the desired target performance. Features: Minimum, maximum, energy, standard deviation (std), mean, signal magnitude area (SMA= 1 T 𝑡 𝑡+𝑇 𝑥 𝑡 𝑑𝑡 + 𝑡 𝑡+𝑇 𝑦 𝑡 𝑑𝑡 + 𝑡 𝑡+𝑇 𝑧 𝑡 𝑑𝑡 )

My Work 4th step : training and classification Method: Training 70% data Model Efficient or not? Standard data Test!! 30% data Classify Sample data Fit which class?

My Work Support vector machine(SVM) – supervised learning models Analyze data and recognize patterns, used for classification and regression analysis. Given a set of training examples, each marked for belonging to one of two categories, an SVM training algorithm builds a model that assigns new examples into one category or the other. Suppose the distance between the classifier and the point is: γ i = 𝑦 𝑖 𝛚 𝐓 ∙ 𝐗 𝑖 +b y i =±1 Ensures that the distance between the classifier and the nearest point be as long as possible, that is max 𝛚,b γ s.t. 𝑦 𝑖 𝛚 𝐓 ∙ 𝐗 𝑖 +b >𝛾 𝑖=1,…, 𝑁 𝛚 =1 Let γ= 1 𝛚 , so it equals to 𝑚𝑖𝑛 𝛚,b 1 2 𝛚 2 s.t. 𝑦 𝑖 𝛚 𝐓 ∙ 𝐗 𝑖 +b >1 𝑖=1,…, 𝑁

My Work From Lagrange duality, we can transform the question into that max α 𝑊 𝛼 = 𝑖=1 𝑁 𝛼 𝑖 − 1 2 𝑖,𝑗=1 𝑁 𝑦 𝑖 𝑦 𝑗 𝛼 𝑖 𝛼 𝑗 < 𝑿 𝑖 , 𝑿 𝑗 > 𝑠.𝑡. 𝛼 𝑖 ≥0, 𝑖=1,…,𝑁 𝑖=1 𝑁 𝛼 𝑖 𝑦 𝑖 =0 (∗) After we figure out α i (SMO algorithm), we have 𝛚= 𝑖=1 𝑁 𝛼 𝑖 𝑦 𝑖 𝑿 𝑖 𝑏=− max i: y i =−1 𝛚 𝐓 𝒙 𝒊 + min i: y i =1 𝛚 𝐓 𝒙 𝒊 2

P=N(N-1)/2 combinations My Work Support vector machine (SVM) Problem: 1. Binary classification 2. Unable to classify more than two actions Solution: One Versus One (OVO) classification Test data send P=N(N-1)/2 combinations N classes P=N(N-1)/2 SVMs vote Final class 需要简单介绍一下SVM及SVM多分类。 以及 判断是否准确的方法：寻找每一类的重心，然后判断距离；或者计算新来数据与所有内置数据之间的最近的几个距离是否在一定阈值之内。 要有测试结果，用矩阵可以表示测试结果。 加油！ ^^

My Work SVM Struct 1 Class 1 1 Class 1 2 4 3 forehand push – 1; backhand push – 2; forehand chop – 3; backhand chop - 4 SVM Struct 1&2 1 1&3 Class 1 1 1&4 2 1 Test data Class 1 3 2&3 2 4 2&4 4 3&4 3

Result Forehand Push Backhand Push Forehand Chop Backhand Chop Recall 67 1 2 95.71% Backhand Push 56 93.33% Forehand Chop 3 62 88.571% 4 45 86.538% Precision 94.37% 91.8% 89.855% 90% 91.270% Precision : the percentage of actions which are correctly detected within all possible detected results. Recall : the percentage of actions which are correctly detected within results that are all determined as that specific action.

Conclusion Successfully collect the data using Android smartphone. Segment the raw data using sliding window. Extract different features of the data. Construct classification model. Successfully classify the test data and tell them whether they are standard data or not.

Future Work The construction of smartband. Acquires sensor data and processes it in the same time. Data transmission between smartband and cell phone. Rewrite the Matlab program into Android.

Thank you! Q&A