├── README.md
├── SignLanguageRecognition.png
├── _config.yml
├── _layouts
    └── default.html
├── csi_phase_offset.py
├── sign_labels.csv
├── signfi_cnn_example.m
├── training_screen_shot.png
└── videos
    ├── 001finish.mp4
    ├── 002go.mp4
    ├── 003help.mp4
    ├── 004like.mp4
    ├── 005love.mp4
    ├── 006more.mp4
    ├── 007need.mp4
    ├── 008no.mp4
    ├── 009not.mp4
    ├── 010please.mp4
    ├── 011right.mp4
    ├── 012sorry.mp4
    ├── 013want.mp4
    ├── 014with.mp4
    ├── 015without.mp4
    ├── 016yes.mp4
    ├── 017animal.mp4
    ├── 018bear.mp4
    ├── 019bird.mp4
    ├── 020cat.mp4
    ├── 021cow.mp4
    ├── 022dog.mp4
    ├── 023elephant.mp4
    ├── 024giraffe.mp4
    ├── 025horse.mp4
    ├── 026lion.mp4
    ├── 027monkey.mp4
    ├── 028pet.mp4
    ├── 029rat.mp4
    ├── 030snake.mp4
    ├── 031tiger.mp4
    ├── 032black.mp4
    ├── 033blue.mp4
    ├── 034brown.mp4
    ├── 035color.mp4
    ├── 036gray.mp4
    ├── 037green.mp4
    ├── 038orange.mp4
    ├── 039pink.mp4
    ├── 040purple.mp4
    ├── 041red.mp4
    ├── 042white.mp4
    ├── 043yellow.mp4
    ├── 044awful.mp4
    ├── 045bad.mp4
    ├── 046bald.mp4
    ├── 047beautiful.mp4
    ├── 048clean.mp4
    ├── 049cold.mp4
    ├── 050cute.mp4
    ├── 051depressed.mp4
    ├── 052different.mp4
    ├── 053excited.mp4
    ├── 054fine.mp4
    ├── 055good.mp4
    ├── 056happy.mp4
    ├── 057hot.mp4
    ├── 058hungry.mp4
    ├── 059hurt.mp4
    ├── 060angry.mp4
    ├── 061mean.mp4
    ├── 062nice.mp4
    ├── 063old.mp4
    ├── 064pissedoff.mp4
    ├── 065rude.mp4
    ├── 066sad.mp4
    ├── 067scared.mp4
    ├── 068slow.mp4
    ├── 069soso.mp4
    ├── 070strong.mp4
    ├── 071tall.mp4
    ├── 072thirsty.mp4
    ├── 073upset.mp4
    ├── 074wonderful.mp4
    ├── 075young.mp4
    ├── 076 aunt.mp4
    ├── 077 baby.mp4
    ├── 078 boyfriend.mp4
    ├── 079 brother.mp4
    ├── 080 children.mp4
    ├── 081 cousinfemale.mp4
    ├── 082 cousinmale.mp4
    ├── 083 date.mp4
    ├── 084 daughter.mp4
    ├── 085 divorce.mp4
    ├── 086 family.mp4
    ├── 087 father.mp4
    ├── 088 friend.mp4
    ├── 089 girlfriend.mp4
    ├── 090 granddaughter.mp4
    ├── 091 grandfather.mp4
    ├── 092 grandmother.mp4
    ├── 093 grandson.mp4
    ├── 094 husband.mp4
    ├── 095 marry.mp4
    ├── 096 mother.mp4
    ├── 097 nephew.mp4
    ├── 098 neice.mp4
    ├── 099 roommate.mp4
    ├── 100 siblings.mp4
    ├── 101 singlealone.mp4
    ├── 102 sister.mp4
    ├── 103 son.mp4
    ├── 104 sweetheart.mp4
    ├── 105 uncle.mp4
    ├── 106 wife.mp4
    ├── 107 apple.mp4
    ├── 108 bacon.mp4
    ├── 109 banana.mp4
    ├── 110 bitter.mp4
    ├── 111 bread.mp4
    ├── 112 breakfast.mp4
    ├── 113 butter.mp4
    ├── 114 candy.mp4
    ├── 115 carrot.mp4
    ├── 116 cheese.mp4
    ├── 117 cherry.mp4
    ├── 118 chicken.mp4
    ├── 119 coffee.mp4
    ├── 120 coke.mp4
    ├── 121 cook.mp4
    ├── 122 cookie.mp4
    ├── 123 delicious.mp4
    ├── 124 dinner.mp4
    ├── 125 drink.mp4
    ├── 126 eat.mp4
    ├── 127 eggs.mp4
    ├── 128 fish.mp4
    ├── 129 food.mp4
    ├── 130 fruit.mp4
    ├── 131 grapes.mp4
    ├── 132 gross.mp4
    ├── 133 hamburger.mp4
    ├── 134 hotdog.mp4
    ├── 135 icecream.mp4
    ├── 136 ketchup.mp4
    ├── 137 lemon.mp4
    ├── 138 lettuce.mp4
    ├── 139 lunch.mp4
    ├── 140 meat.mp4
    ├── 141 milk.mp4
    ├── 142 mushroom.mp4
    ├── 143 nuts.mp4
    ├── 144 onion.mp4
    ├── 145 peach.mp4
    ├── 146 pepper.mp4
    ├── 147 pineapple2.mp4
    ├── 148 pizza.mp4
    ├── 149 potato.mp4
    ├── 150 salt.mp4
    ├── 151 sandwich.mp4
    ├── 152 sodapop.mp4
    ├── 153 strawberry.mp4
    ├── 154 sugar.mp4
    ├── 155 sweet.mp4
    ├── 156 tea.mp4
    ├── 157 tomato.mp4
    ├── 158 turkey.mp4
    ├── 159 vegetables.mp4
    ├── 160 water.mp4
    ├── 161 apartment.mp4
    ├── 162 bathroom.mp4
    ├── 163 bicycle.mp4
    ├── 164 buy.mp4
    ├── 165 car.mp4
    ├── 166 diningroom.mp4
    ├── 167 door.mp4
    ├── 168 garage.mp4
    ├── 169 goenter.mp4
    ├── 170 home.mp4
    ├── 171 house.mp4
    ├── 172 kitchen.mp4
    ├── 173 room.mp4
    ├── 174 shower.mp4
    ├── 175 sofa.mp4
    ├── 176 telephone.mp4
    ├── 177 toilet.mp4
    ├── 178 walk.mp4
    ├── 179 askyou.mp4
    ├── 180 boy.mp4
    ├── 181 deaf.mp4
    ├── 182 girl.mp4
    ├── 183 giveyou.mp4
    ├── 184 hearing.mp4
    ├── 185 me.mp4
    ├── 186 man.mp4
    ├── 187 my.mp4
    ├── 188 woman.mp4
    ├── 189 work.mp4
    ├── 190 you.mp4
    ├── 191 your.mp4
    ├── 192 how.mp4
    ├── 193 what.mp4
    ├── 194 when.mp4
    ├── 195 where.mp4
    ├── 196 who.mp4
    ├── 197 why.mp4
    ├── 198 book.mp4
    ├── 199 bus2.mp4
    ├── 200 class.mp4
    ├── 201 college.mp4
    ├── 202 dorm.mp4
    ├── 203 draw.mp4
    ├── 204 elementaryschool.mp4
    ├── 205 english.mp4
    ├── 206 highschool.mp4
    ├── 207 history.mp4
    ├── 208 know.mp4
    ├── 209 learn.mp4
    ├── 210 math.mp4
    ├── 211 paper.mp4
    ├── 212 play.mp4
    ├── 213 read.mp4
    ├── 214 school.mp4
    ├── 215 science.mp4
    ├── 216 sentence.mp4
    ├── 217 sign.mp4
    ├── 218 sports.mp4
    ├── 219 student.mp4
    ├── 220 teacher.mp4
    ├── 221 university.mp4
    ├── 222 write.mp4
    ├── 223 after.mp4
    ├── 224 afternoon.mp4
    ├── 225 again.mp4
    ├── 226 always.mp4
    ├── 227 before.mp4
    ├── 228 day.mp4
    ├── 229 friday.mp4
    ├── 230 future.mp4
    ├── 231 hour.mp4
    ├── 232 lastpast.mp4
    ├── 233 midnight.mp4
    ├── 234 minute.mp4
    ├── 235 monday.mp4
    ├── 236 month.mp4
    ├── 237 morning.mp4
    ├── 238 never.mp4
    ├── 239 night.mp4
    ├── 240 noon.mp4
    ├── 241 now.mp4
    ├── 242 saturday.mp4
    ├── 243 sometimes.mp4
    ├── 244 sunday.mp4
    ├── 245 thursday.mp4
    ├── 246 time.mp4
    ├── 247 today.mp4
    ├── 248 tomorrow.mp4
    ├── 249 tuesday.mp4
    ├── 250 wednesday.mp4
    ├── 251 week.mp4
    ├── 252 year.mp4
    ├── 253 yesterday.mp4
    ├── 254ac.mp4
    ├── 255body.mp4
    ├── 256leg.mp4
    ├── 257fan.mp4
    ├── 258radio.mp4
    ├── 259tv.mp4
    ├── 260refrigerator.mp4
    ├── 261browser.mp4
    ├── 262window.mp4
    ├── 263washer.mp4
    ├── 264computer.mp4
    ├── 265left.mp4
    ├── 266right.mp4
    ├── 267up.mp4
    ├── 268down.mp4
    ├── 269start.mp4
    ├── 270stop.mp4
    ├── 271next.mp4
    ├── 272previous.mp4
    ├── 273zoom.mp4
    ├── 274open.mp4
    ├── 275close.mp4
    └── 276continue.mp4


/README.md:
--------------------------------------------------------------------------------
  1 | This website contains datasets of Channel State Information (CSI) traces for sign language recognition using WiFi.
  2 | 
  3 | ## Cite the Paper
  4 | > Yongsen Ma, Gang Zhou, Shuangquan Wang, Hongyang Zhao, and Woosub Jung. 2018. SignFi: Sign Language Recognition Using WiFi. Proc. ACM Interact. Mob. Wearable Ubiquitous Technol. 2, 1, Article 23 (March 2018), 21 pages. DOI: [https://doi.org/10.1145/3191755](https://doi.org/10.1145/3191755)
  5 | 
  6 | Readers can also check the following paper for more details about WiFi sensing with Channel State Information.
  7 | > Yongsen Ma, Gang Zhou, and Shuangquan Wang. 2019. WiFi Sensing with Channel State Information: A Survey. ACM Comput. Surv. 52, 3, Article 46 (June 2019), 36 pages. DOI: [https://doi.org/10.1145/3310194](https://doi.org/10.1145/3310194)
  8 | 
  9 | 
 10 | ## Files
 11 | This repository contains the following files. You agree to the [Terms of Use for SignFi Dataset](#terms-of-use-for-signfi-dataset) to download and use the files.
 12 | 
 13 | | Files | Description | Size |
 14 | | ----- | ----------- | ---- |
 15 | | [dataset_lab_276_dl.mat](https://wm1693.box.com/s/z9vsrn3998n4xyzkpqtj89yclk28eatp) | Segmented downlink CSI traces and ground truth labels for 276 sign words in the lab environment. There are 5,520 instances of 276 sign gestures performed by one user.| 1.44GB |
 16 | | [dataset_lab_276_ul.mat](https://wm1693.box.com/s/5hr4u7lsj10c329oibp8fjv95i524tnl) | Segmented uplink CSI traces and ground truth labels for 276 sign words in the lab environment. There are 5,520 instances of 276 sign gestures performed by one user.| 1.33GB |
 17 | | [dataset_home_276.mat](https://wm1693.box.com/s/mmikgi9ubkg7vnwaztplnxudh8sgj1np) | Segmented downlink and uplink CSI traces and ground truth labels for 276 sign words in the home environment. There are 2,760 instances of 276 sign gestures performed by one user. | 1.37GB |
 18 | | [dataset_lab_150.mat](https://wm1693.box.com/s/kidoq54rv93ysojgzv7xjqixyzwir7lq) | Segmented downlink CSI traces and ground truth labels for 150 sign words in the lab environment. There are 7,500 instances of 150 sign gestures performed by five users. | 1.93GB |
 19 | | [signfi_cnn_example.m](https://wm1693.box.com/s/pvlrxb7cxexgfquyt1dqn52a49kz90db) | MATLAB source code for training and testing using the dataset. | 3KB |
 20 | | [training_screen_shot.png](https://wm1693.box.com/s/4vkpfzet9cctpya8pcjorq646adomboe) | A screen shot of the training process | 541KB |
 21 | | [sign_labels.csv](https://wm1693.box.com/s/wu3bvgbuzbypfvsq716qqgynpw8qriiy) | 276 sign words used in the measurement. | 2KB |
 22 | | [videos/](https://wm1693.box.com/s/ptdahj91p3uaxm49fz24b398xxzu7yl7) | This folder contains the videos for 276 basic sign words. These videos are used for the participant to learn and perform sign gestures during data collection. | 105.9MB |
 23 | | [README.md](https://wm1693.box.com/s/jx4t4aeg5gm3xhnh8v5ooj7cr6qb3xgv) | Readme | 5KB |
 24 | 
 25 | Both downlink and uplink CSI traces are included for the 276 sign words in the lab and home environments. Only downlink CSI traces are used in the paper. CSI traces are collected using the <a href="http://dhalperi.github.io/linux-80211n-csitool/" target="_blank">802.11n CSI Tool</a>. Please check the paper for more details about experiment setup, measurement procedure, WiFi settings, etc.
 26 | 
 27 | 
 28 | ## An Example
 29 | The following shows an example of how to train the dataset. Load the dataset `csid_lab` and `label_lab` into the Matlab workspace by running `load('dataset_lab_276_dl.mat');`. Run `[net_info, perf] = signfi_cnn_example(csid_lab,label_lab);` to train the neural network and get recognition results. This shows the training process in the following figure.
 30 | ![Training process](./training_screen_shot.png)
 31 | 
 32 | The following shows how `signfi_cnn_example` works.
 33 | 1. Prepare data for training.
 34 | ```matlab
 35 | tic; % starting time
 36 | csi_abs = abs(csi); % CSI amplitude
 37 | csi_ang = angle(csi); % CSI phase
 38 | csi_tensor = [csi_abs,csi_ang];
 39 | word = categorical(label);
 40 | t0 = toc; % pre-processing time
 41 | ```
 42 | `tic` and `toc` are used to measure time consumption. The size of `csid_lab` is `size(csid_lab)=(200,30,3,5520)`. `csi_tensor` and `word` are the CSI input and labels. (Optional) Remove phase offset before feeding to neural network models. A reference of the code for removing phase offset can be found at [csi_phase_offset.py](./csi_phase_offset.py).
 43 | ```python
 44 | from scipy.optimize import curve_fit
 45 | idx_tx_subc = np.zeros(shape=(2, 3, 3, 30)) # placeholder for curve fitting
 46 | for tx in range(3):         # tx index
 47 |     for rx in range(3):     # rx index
 48 |         for k in range(30): # subcarrier index
 49 |             idx_tx_subc[0, tx, rx, k] = (tx+2)/3 - 2 # tx index, reordered
 50 |             idx_tx_subc[1, tx, rx, k] = -58 + 4*k # subcarrier index
 51 | idx_tx_subc = np.reshape(idx_tx_subc, (2, 270))
 52 | ```
 53 | ```python
 54 | def func(x, a, b, c): # Phase offsets function
 55 |     return a*x[0] *x[1] + b*x[1] + c # x[0]: transmit antenna index, x[1]: subcarrier index
 56 | ```
 57 | ```python
 58 | def remove_phase_offset(csi):
 59 |     csi = np.array(csi)
 60 |     csi_shape = csi.shape
 61 |     csi_abs = np.abs(csi)
 62 |     csi_ang = np.angle(csi)
 63 |     phase = csi_ang[0, ...] # get phase offsets from the 1st packet
 64 |     for tx in range(csi_shape[1]): # tx
 65 |         for rx in range(csi_shape[2]): # rx
 66 |             phase[tx, rx, :] = unwrap(phase[tx, rx, :])
 67 |     phase = phase. flatten()
 68 |     popt, pcov = curve_fit(func, idx_tx_subc, phase)
 69 |     phase = func(idx_tx_subc, *popt)
 70 |     phase = np.reshape(phase, (csi_shape(1),csi_shape [ 2 ], csi_shape [3]))
 71 |     pdp = np.zeros(shape=csi_shape, dtype=np.complex)
 72 |     for t in range (csi_shape[0]): # time
 73 |         for tx in range(csi_shape[1]): # tx
 74 |             for rx in range(csi_shape[2]): # rx
 75 |                 csi_ang[t, tx, rx, :] = unwrap(csi_ang[t, tx, rx, :])
 76 |                 csi_ang[t, tx, rx, :] -= phase[tx, rx, :]
 77 |                 csi_new = csi_abs[t, tx, rx, : ]*np.exp(1j*csi_ang[t, tx, rx, :])
 78 |                 pdp[t, tx,rx, :] = np.fft.ifft(csi_new)
 79 |     return csi_abs, csi_ang, pdp
 80 | ```
 81 | 
 82 | 2. Some parameter settings.
 83 | ```matlab
 84 | % [M,N,S]: CSI matrix for each instance
 85 | % T: the total number of instances
 86 | [M,N,S,T] = size(csi_tensor);
 87 | Nw = 276; % number of classes
 88 | rng(42); % For reproducibility
 89 | n_epoch = 10;
 90 | learn_rate = 0.01;
 91 | l2_factor = 0.01;
 92 | ```
 93 | 
 94 | 3. Divide the dataset into training and testing subsets.
 95 | ```matlab
 96 | K = 5;
 97 | cv = cvpartition(T,'kfold',K); % 20% for testing
 98 | k = 1; % for k=1:K
 99 | trainIdx = find(training(cv,k));
100 | testIdx = find(test(cv,k));
101 | trainCsi = csi_tensor(:,:,:,trainIdx);
102 | trainWord = word(trainIdx,1);
103 | testCsi = csi_tensor(:,:,:,testIdx);
104 | testWord = word(testIdx,1);
105 | valData = {testCsi,testWord};
106 | ```
107 | 
108 | 4. Set neural network layers and training options. MATLAB 2017b or newer versions are needed to use `batchNormalizationLayer()` for network layers and `ValidationData` for training options. For `trainingOptions()`, `'ExecutionEnvironmnet'` can be `'cpu'`, `'gpu'`, or `'parallel'`.
109 | ```matlab
110 | % Neural network layer settings
111 | layers = [imageInputLayer([M N S]);
112 |           convolution2dLayer(4,4,'Padding',0);
113 |           batchNormalizationLayer();
114 |           reluLayer();
115 |           maxPooling2dLayer(4,'Stride',4); 
116 |           fullyConnectedLayer(Nw);
117 |           softmaxLayer();
118 |           classificationLayer()];
119 | % Neural network training options
120 | options = trainingOptions('sgdm','ExecutionEnvironment','cpu',...
121 |                           'MaxEpochs',n_epoch,...
122 |                           'InitialLearnRate',learn_rate,...
123 |                           'L2Regularization',l2_factor,...
124 |                           'ValidationData',valData,...
125 |                           'ValidationFrequency',10,...
126 |                           'ValidationPatience',Inf,...
127 |                           'Shuffle','every-epoch',...
128 |                           'Verbose',false,...
129 |                           'Plots','training-progress');
130 | ```
131 | 5. Train and test the neural network; calculate recognition accuracy.
132 | ```matlab
133 | [trainedNet,tr{k,1}] = trainNetwork(trainCsi,trainWord,layers,options);
134 | t1 = toc; % training end time
135 | [YTest, scores] = classify(trainedNet,testCsi);
136 | TTest = testWord;
137 | test_accuracy = sum(YTest == TTest)/numel(TTest);
138 | t2 = toc; % testing end time
139 | net_info = tr;
140 | perf = test_accuracy;
141 | ```
142 | 
143 | 6. Plot the confusion matrix. Since there are too many classes, it is not reasonable to plot the whole confusion matrix. You can divide the classes into different categories and then plot the confusion matrix.
144 | ```matlab
145 | % plot confusion matrix
146 | ttest = dummyvar(double(TTest))';
147 | tpredict = dummyvar(double(YTest))';
148 | [c,cm,ind,per] = confusion(ttest,tpredict);
149 | plotconfusion(ttest,tpredict);
150 | ```
151 | 
152 | A comparision of sign language recognition technologies is shown in the following figure.
153 | 
154 | ![Sign Language Recognition Comparison](./SignLanguageRecognition.png)
155 | 
156 | ## Terms of Use for SignFi Dataset
157 | 
158 | You need to read and agree to the following terms of use to download and use the SignFi dataset.
159 | 
160 | >`1. Definitions`
161 | >
162 | >The following terms, unless the context requires otherwise, have the following meanings:
163 | >
164 | >“Data Team”: means the employees and students at the College of William and Mary who are working on the SignFi dataset;
165 | >
166 | >“SignFi Dataset”: means the channel state information traces collected by the Data Team;
167 | >
168 | >“Licensee”, “You”, “Your”, “Yours”: means the person or entity acquiring a license hereunder for access to and use of the SignFi Dataset.
169 | 
170 | >
171 | >`2. Grant of License`
172 | >
173 | >The College of William and Mary hereby grants to You a non-exclusive, non-transferable, revocable license to use the SignFi Dataset solely for Your non-commercial, educational, and research purposes only, but without any right to copy or reproduce, publish or otherwise make available to the public or communicate to the public, sell, rent or lend the whole or any constituent part of the SignFi Dataset thereof. The SignFi Dataset shall not be redistributed without the express written prior approval of the College of William and Mary You agree to respect the privacy of those human subjects whose sign gesture data are included in the SignFi Dataset. Do not attempt to reverse the anonymization process to identify specific identifiers including, without limitation, names, postal address information, telephone numbers, e-mail addresses, social security numbers, and biometric identifiers. You agree not to reverse engineer, separate or otherwise tamper with the SignFi Dataset so that data can be extracted and used outside the scope of that permitted in this Agreement.
174 | >
175 | >You agree to acknowledge the source of the SignFi Dataset in all of Your publications and presentations based wholly or in part on the SignFi Dataset. You agree to provide a disclaimer in any publication or presentation to the effect that the College of William and Mary does not bear any responsibility for Your analysis or interpretation of SignFi Dataset.
176 | >
177 | >You agree and acknowledge that the College of William and Mary may hold, process, and store any personal data submitted by You for validation and statistical purposes and for the purposes of the administration and management of SignFi Dataset. You agree that any personal data submitted by You is accurate to the best of his or her knowledge.
178 | >
179 | >The College of William and Mary provides the SignFi Dataset "AS IS," without any warranty or promise of technical support, and disclaims any liability of any kind for any damages whatsoever resulting from use of the SignFi Dataset .
180 | >
181 | >THE COLLLEGE OF WILLIAM AND MARY MAKES NO WARRANTIES, EXPRESS OR IMPLIED WITH RESPECT TO THE SIGNFI DATASET, INCLUDING ANY IMPLIED WARRANTY OF MERCHANTABILITY OR FITNESS FOR A PARTICULAR PURPOSE, WHICH ARE HEREBY EXPRESSLY DISCLAIMED.
182 | >
183 | >Your acceptance and use of the SignFi Dataset binds you to the terms and conditions of this License as stated herein.
184 | 


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/SignLanguageRecognition.png:
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https://raw.githubusercontent.com/yongsen/SignFi/0e3ecd3082566b9090c5f400311d53a0ac10bdfc/SignLanguageRecognition.png


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66 | 


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/csi_phase_offset.py:
--------------------------------------------------------------------------------
 1 | from scipy.optimize import curve_fit
 2 | 
 3 | # x (tx and subcarrier index) for curve fitting
 4 | idx_tx_subc = np.zeros(shape=(2, 3, 3, 30))
 5 | for tx in range(3):
 6 |     for rx in range(3):
 7 |         for k in range(30):
 8 |             idx_tx_subc[0, tx, rx, k] = (tx+2)/3 - 2 # tx index, reordered
 9 |             idx_tx_subc[1, tx, rx, k] = -58 + 4*k # subcarrier index
10 | idx_tx_subc = np.reshape(idx_tx_subc, (2, 270))
11 | 
12 | def func(x, a, b, c):
13 |     """Phase offsets function
14 |     x[0]: transmit antenna index
15 |     x[1]: subcarrier index
16 |     """
17 |     return a*x[0] *x[1] + b*x[1] + c
18 | 
19 | def remove_phase_offset(csi):
20 |     csi = np.array(csi)
21 |     csi_shape = csi.shape
22 |     csi_abs = np.abs(csi)
23 |     csi_ang = np.angle(csi)
24 |     phase = csi_ang[0, ...] # get phase offsets from the 1st packet
25 |     for tx in range(csi_shape[1]): # tx
26 |         for rx in range(csi_shape[2]): # rx
27 |             phase[tx, rx, :] = unwrap(phase[tx, rx, :])
28 | 
29 |     phase = phase. flatten()
30 |     popt, pcov = curve_fit(func, idx_tx_subc, phase)
31 | 
32 |     phase = func(idx_tx_subc, *popt)
33 |     phase = np.reshape(phase, (csi_shape(1),csi_shape [ 2 ], csi_shape [3]))
34 | 
35 |     pdp = np.zeros(shape=csi_shape, dtype=np.complex)
36 |     for t in range (csi_shape[0]): # time
37 |         for tx in range(csi_shape[1]): # tx
38 |             for rx in range(csi_shape[2]): # rx
39 |                 csi_ang[t, tx, rx, :] = unwrap(csi_ang[t, tx, rx, :])
40 |                 csi_ang[t, tx, rx, :] -= phase[tx, rx, :]
41 | 
42 |                 csi_new = csi_abs[t, tx, rx, : ]*np.exp(1j*csi_ang[t, tx, rx, :])
43 |                 pdp[t, tx,rx, :] = np.fft.ifft(csi_new)
44 |     return csi_abs, csi_ang, pdp
45 | 


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/sign_labels.csv:
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1 | FINISH
GO
HELP
LIKE
LOVE
MORE
NEED
NO
NOT
PLEASE
RIGHT
SORRY
WANT
WITH
WITHOUT
YES
ANIMAL
BEAR
BIRD
CAT
COW
DOG
ELEPHANT
GIRAFFE
HORSE
LION
MONKEY
PET
RAT
SNAKE
TIGER
BLACK
BLUE
BROWN
COLOR
GRAY
GREEN
ORANGE
PINK
PURPLE
RED
WHITE
YELLOW
AWFUL
BAD
BALD
BEAUTIFUL
CLEAN
COLD
CUTE
DEPRESSED
DIFFERENT
EXCITED
FINE
GOOD
HAPPY
HOT
HUNGRY
HURT
MAD/ANGRY
MEAN
NICE
OLD
PISSED OFF
RUDE
SAD
SCARED
SLOW
SO-SO
STRONG
TALL
THIRSTY
UPSET
WONDERFUL
YOUNG
AUNT
BABY
BOYFRIEND
BROTHER
CHILDREN
COUSIN (female)
COUSIN (male)
DATE
DAUGHTER
DIVORCE
FAMILY
FATHER
FRIEND
GIRLFRIEND
GRANDDAUGHTER
GRANDFATHER
GRANDMOTHER
GRANDSON
HUSBAND
MARRY
MOTHER
NEPHEW
NIECE
ROOMMATE
SIBLINGS
SINGLE
SISTER
SON
SWEETHEART
UNCLE
WIFE
APPLE
BACON
BANANA
BITTER
BREAD
BREAKFAST
BUTTER
CANDY
CARROT
CHEESE
CHERRY
CHICKEN
COFFEE
COKE (Coca Cola)
COOK
COOKIE
DELICIOUS
DINNER
DRINK
EAT
EGGS
FISH
FOOD
FRUIT
GRAPES
GROSS
HAMBURGER
HOTDOG
ICE CREAM
KETCHUP
LEMON
LETTUCE
LUNCH
MEAT
MILK
MUSHROOM
NUTS
ONION
PEACH
PEPPER
PINEAPPLE
PIZZA
POTATO
SALT
SANDWICH
SODA-POP
STRAWBERRIES
SUGAR
SWEET
TEA
TOMATO
TURKEY
VEGETABLES
WATER
APARTMENT
BATHROOM
BICYCLE
BUY
CAR
CLEAN
DOOR
GARAGE
GO-IN/ENTER
HOME
HOUSE
KITCHEN
ROOM
SHOWER
SOFA
TELEPHONE
TOILET
WALK
ASK-YOU
BOY
DEAF
GIRL
GIVE-YOU
HEARING
I/ME
MAN
MY/MINE
WOMAN
WORK
YOU
YOUR
HOW
WHAT
WHEN
WHERE
WHO
WHY
BOOK
BUS
CLASS
COLLEGE
DORM
DRAW
ELEMENTARY
ENGLISH
HIGH-SCHOOL
HISTORY
KNOW
LEARN
MATH
PAPER
PLAY
READ
SCHOOL
SCIENCE
SENTENCE
SIGN
SPORTS
STUDENT
TEACHER
UNIVERSITY
WRITE
AFTER
AFTERNOON
AGAIN
ALWAYS
BEFORE
DAY
FRIDAY
FUTURE
HOUR
LAST/PAST
MIDNIGHT
MINUTE
MONDAY
MONTH
MORNING
NEVER
NIGHT/EVENING
NOON
NOW
SATURDAY
SOMETIMES
SUNDAY
THURSDAY
TIME
TODAY
TOMORROW
TUESDAY
WEDNESDAY
WEEK
YEAR
YESTERDAY
Air Conditioner
BODY
LEG
FAN
RADIO
TV
REFRIGERATOR
BROWSER
WINDOW
WASHER
COMPUTER
LEFT (direction)
RIGHT (direction)
UP
DOWN
START
STOP
NEXT
PREVIOUS
ZOOM
OPEN
CLOSE
CONTINUE


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/signfi_cnn_example.m:
--------------------------------------------------------------------------------
 1 | %% Yongsen Ma <yma@cs.wm.edu>
 2 | % Computer Science Department, The College of William & Mary
 3 | %
 4 | % This is an example for the following paper
 5 | % Yongsen Ma, Gang Zhou, Shuangquan Wang, Hongyang Zhao, and Woosub Jung. 2018.
 6 | % SignFi: Sign Language Recognition Using WiFi.
 7 | % Proc. ACM Interact. Mob. Wearable Ubiquitous Technol. 2, 1, Article 23 (March 2018), 21 pages.
 8 | % DOI: https://doi.org/10.1145/3191755
 9 | 
10 | function [net_info, perf] = signfi_cnn_example(csi,label)
11 |     % load('dataset_lab_276_dl.mat'); % load CSI and labels into workspace
12 |     tic; % time of starting
13 |     % prepare for training data
14 |     csi_abs = abs(csi);
15 |     csi_ang = angle(csi);
16 |     % csi_ang = get_signfi_phase(csi);
17 |     csi_tensor = [csi_abs,csi_ang];
18 |     word = categorical(label);
19 |     t0 = toc; % pre-processing time
20 |     
21 |     % [M,N,S]: CSI matrix for each instance
22 |     % T: the total number of instances
23 |     [M,N,S,T] = size(csi_tensor);
24 |     Nw = 276; % number of classes
25 |     
26 |     rng(42); % For reproducibility
27 |     n_epoch = 10;
28 |     learn_rate = 0.01;
29 |     l2_factor = 0.01;
30 |     
31 |     % Convolutional Neural Network settings
32 |     layers = [imageInputLayer([M N S]);
33 |               convolution2dLayer(4,4,'Padding',0);
34 |               batchNormalizationLayer();
35 |               reluLayer();
36 |               maxPooling2dLayer(4,'Stride',4); 
37 |               fullyConnectedLayer(Nw);
38 |               softmaxLayer();
39 |               classificationLayer()];
40 |                          
41 |     % get training/testing input
42 |     K = 5;
43 |     cv = cvpartition(T,'kfold',K); % 20% for testing
44 |     k = 1; % for k=1:K
45 |     trainIdx = find(training(cv,k));
46 |     testIdx = find(test(cv,k));
47 |     trainCsi = csi_tensor(:,:,:,trainIdx);
48 |     trainWord = word(trainIdx,1);
49 |     testCsi = csi_tensor(:,:,:,testIdx);
50 |     testWord = word(testIdx,1);
51 |     valData = {testCsi,testWord};
52 |     
53 |     % training options for the Convolutional Neural Network
54 |     options = trainingOptions('sgdm','ExecutionEnvironment','cpu',...
55 |                           'MaxEpochs',n_epoch,...
56 |                           'InitialLearnRate',learn_rate,...
57 |                           'L2Regularization',l2_factor,...
58 |                           'ValidationData',valData,...
59 |                           'ValidationFrequency',10,...
60 |                           'ValidationPatience',Inf,...
61 |                           'Shuffle','every-epoch',...
62 |                           'Verbose',false,...
63 |                           'Plots','training-progress');
64 | 
65 |     [trainedNet,tr{k,1}] = trainNetwork(trainCsi,trainWord,layers,options);
66 |     t1 = toc; % training end time
67 | 
68 |     [YTest, scores] = classify(trainedNet,testCsi);
69 |     TTest = testWord;
70 |     test_accuracy = sum(YTest == TTest)/numel(TTest);
71 |     t2 = toc; % testing end time
72 |     
73 |     % plot confusion matrix
74 | %     ttest = dummyvar(double(TTest))';
75 | %     tpredict = dummyvar(double(YTest))';
76 | %     [c,cm,ind,per] = confusion(ttest,tpredict);
77 | %     plotconfusion(ttest,tpredict);
78 | 
79 |     net_info = tr;
80 |     perf = test_accuracy;
81 | end
82 | 


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