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ME220 A Basic Algorithm
Creation date：2024-05-06 14:34:00
Belonging category：计算机computer science
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A Basic Algorithm

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ME220 


In this Lab you will attempt to use inertial data to make a basic step counter. Inertial sensing-
based step counting is used commercially in standalone step counting products and has been
implemented into the health tracking features in many smartphone platforms. You will first make
a pedometer using a basic step counting algorithm, and then develop your own more complex
algorithm to handle more practical cases.

For this lab you may work in pairs and submit one lab report per group.

Part 1: A Basic Algorithm

1. Use phyphox to record three axis acceleration data with your phone in your pocket while
you walk around for about 30 seconds. Count the number of steps you took while
recording this data. Export the result and trim out any data at the start or end that does
not contain steps.
2. Plot the acceleration for each axis, and the value of the acceleration magnitude, over
time. (4 plots). What signatures of individual steps do you see?
3. Find the ASD of the acceleration magnitude. Comment on the step signal’s amplitude
and frequency.
4. Design a filter that allows you to separate the steps from other signals (such as the
acceleration of gravity). Justify your choice of filter type and cutoff frequency. Apply this
filter to the acceleration magnitude time series and plot the result.
5. Implement an algorithm that identifies a step by counting every time the filtered
acceleration crosses a certain threshold. Pick a threshold based on your plot in part 4.
6. How many steps does your algorithm identify? Make a plot showing the filtered
acceleration with each detected step annotated. Is this an over or under count compared
to the amount you counted manually? Comment on any misclassification.
7. After everything is working, record acceleration data while walking at least 5 more times,
and compare the estimated number of steps with the actual number of steps. If you’re
working with a partner, does the algorithm properly count steps for both of you?

Make sure to include the following items in your report:
• The plots in parts 1, 3, 4 and 6
• Your answers to all questions posed
• Any code used to do calculations and generate plots

Part 2: Beyond Thresholding
More complex methods for step counting are well known and have been implemented in
smartphones as part of the health monitoring suits common on personal devices. In this
application, the user exposes the sensor to many signals, from which steps have to be
extracted. Approaches to this problem can be seen in many references, some of which have
been included in with this lab and are excerpted below: These methods distinguish between
steps and other signals that result in a large acceleration like sitting down or moving holding the
sensor and moving it around. Human strides occur at a frequency around 1-2 Hz and induce
acceleration with a particular orientation with respect to gravity. By using these facts, it is
possible to better extract the signal of human footsteps.

1. Again, use phyphox to record three axis acceleration data with your phone in your
pocket while you walk around, but this time include sitting down, standing up, and
standing still during the collection period. Count the number of steps you took while
recording this data. Export the result, and this time do not trim the data.
2. Repeat this process but place the phone in a bag or backpack. Export the result.
3. Filter the acceleration magnitude from both data sets and plot it. Try to label the times
when you were walking, and when you were standing still. How can you distinguish a
step from other motions like sitting down, or moving around while sitting?
4. Run the algorithm developed in Part 1 on both new data sets. Annotate the steps it
identifies on your plot from question 3. How does it perform? Investigate the data around
any miscounting events and comment on why they occurred.
5. Based on the references above, develop a more complex algorithm to count steps that
performs better than the basic threshold method. Your algorithm should:
• Be able to distinguish between steps and other acceleration spike events like
sitting down, standing up, or jostling.
• Function with the phone positioned in any orientation, whether placed in a
pocket or in a bag.
• Detect steps of different size or gait
Lee (2015) Naqvi (2012)
Myo (2018)
Use a method inspired by the resources included with the lab, or by your own research
on the subject matter.
6. Compare your improved algorithm to the threshold algorithm on the basic data set taken
in Part 1. Does your improved method perform better in areas where the threshold
method miscounted?
7. Compare your improved algorithm to the threshold algorithm on the data sets taken in
Part 2. How does your algorithm perform? Comment on any over or undercounts? Does
your improved method work better when the phone is in a pocket, or in a bag?

Make sure to include the following items in your report:
• The plots and annotations from parts 3 and 4
• A detailed description of the algorithm you chose to implement. Include plots and
examples of individual components of the method.
• Your answers to all questions posed
• Any code used to do calculations and generate plots
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关键词一 关键词二 关键词三 关键词四 关键词五 和法规和的发更好的风格和 ECON 615 FINC 612 CSC148 COMP 639 ACCT 203 ACCT 211 PSYC 101 MTH2009 COMP52315 PSYCO 432 statistics MATH4063 11 xxx 1 MA1MSP MAST30028 MATH3888 CS341 EEE30002 ES2F5 MCD4160 CC0933 ECON 1101 MCHM3001 CHEM3118 COMMGMT 2511 COMP41280 STAT 631 24T1 IRE379 SENG365 ECON433 FIT3139 MATH4602 LING5621M FINS5516 ECON3208 MN2177 L2 SM EEET1415 Finance DS4023 COMP3004 7CCSMRTS K1S5B6 Stat230 CSCI4211 CSI2132 ECON30130 MAS6100 ENVS222 COM2107 INFO1112 STAT 4004 FIT2100 comp2022 COMP4691 Physics 307 Econ 659 probability CSCI316 fir29 CSCI203 COMP3160 COMP6714 INFO1113 Physics 7B Physics Photonics S203031 STAT2005 COMP90015 CSCI 2110 CSCC46 Information CSC477 COMP4650 Statistical COMP 346 COMP1017 ELEC340 MATH1007 Programming function STAT 244 CS 134 Java cse13s Math 108A FW20 ECON3389 CSE 130 Science 331 COMP9020 engineering COSC 445 CSE 5523 GAME2012 visualization MATH 235 Math 370 CSC343 COGS 108 EE524 COMP10200 STAT 416 3FK4 MSCI 311 ECON6113 ACC40700 EMESTER1 MAS8383 ECO 512 STAT 8310 CMN3102 MA577 CMPT 225 COMP0045 Methods Equations MATH 113 Math 3283W ICS-33 EN4302/ENT603 BIA B350F MA318 MTHM506 MECH 5315M ECONOMICS MAS8382 COM 2004 PX3142 XJEL 3662 STAT 231 MGT B399F PX4131 COM2108 30AM MSCI521 ECONM1015 ACFI304 GR5241 MATH10101 ECON 2070 COMS31900 EFIM20011 CSE 250B ACSE-5 STAT4207 SOFT2412 ELEC 331 AY2020 MSIN0209 FIT2094 CS 241L ST404 CS 411 3D CA3 INF4002 COMP3506 SIT718 CS 188 CS 170 MATH 138 CSE 251A CMT118 DATA5207 CMPT307 CMPT459 T1 PGEE11108 COSC 3P32 ME 163 MATH96007 7QQMM203 EE6227 BA 574 STA 4234 ECO206Y1Y ECO2008 MT2300 MATH 11158 CS 527 MACM 316 CSE474 2DI70 ISE 525 COMP3411 EMAT30007 7CCSMBDT MFIN704 MFIN704W21 MfIB ECE 438 CS 436 F36 CS5344 FE 621A FE621 TBA 612 ECM3151 ECMM422 MF1 MSIN0107 ESSMENT CS 5854 MFIT 5008 MIPS R4000 ENG5274 BU52018 ENG2079 BIOA02 STA2570 F0 INF553 STAT 440 STAT3008 COMP9417 COMP6451 6COM1042 BU52006 ST303 HW-1 CISC-235 CS 510 MATH3161 ECN 140 ECE391 Comp 3613 CVEN 9625 PRG455 UESTION 1 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CMP7000A CMP-7038B Math 263 LAW3016 CS371 STAT 334 FIN0008 ACC3004 STAT 371 ​COMP1921 MTH201 ACCT2000 UN3412 CIV6000 MATHS 3021 MATE7014 ECON1 CIBC6035 LAW121G COMM 321 CSC8204R TTM2033 CSCI 2600 MAE 115 LNG302 CENV6175W1 ECON30019 CPS2015 BST969 DDES9015 AMATH242 CS 1151 N1592 ACCT600 EG503G SMM519 EC318 Ecstatistics EEE 471 MARK2051 Topology COM00037H ACCT3000 MTHM501 EG501V COM00055H BUSN9085 GGR273 MATH 2B ITD102 MA214 ECON0060 ECE3114 ECMM447 Mechanics CCT361H5S BSAN3210 WELF2001 Fin3001 RESP5001 STA 137 Linguistics 101 CSCI368 CMPSC497 LUBS3655 OMBA 5355 BUSM2562 ​BFF5902 CYBR7001 CULT2005 ECON 7720 MTRX3760 FSE598 ​LUE1001 MGSC 7100 CS3240 COSC7500 ACCT3091 INFOGR TRAN 2080 DESIGN CIVE215001 CIVE2360 CPSC 8430 FNCE2003 Stoichiometry INFS6023 Quantitative Method Math 21D ECE5179 Design ​ECON8022 Chemical DMS 213 ADM1370 BFF5902 BFF2701 AGRI10051 AMN400 ECON7322 ​FINC5090 ​MATH1052 ​SciComp Elec4622 ​FINS5516 ENG 101 MMM143 CS431CA2 BSAN2201 EBSC7070 ES9v9-10 PLIN0009 DATA 8 MATH 101 BCPM0097 ​MAT 237 BISM2201 EEE8099 Physics 11 ECE2238B Biostat 234 ​ECON30009 INF10025 CHEM252 CCMA4008 CIVE 3007 STAT0031 ELEC ENG 3108 MGMT90140 CHEM 252 STAT3016 MMM095 ​MMGT6016 Legal STAT 311 ELEC9764 COMP 370 TCS 3053 EECS 1021 FIT1051 C/C++ EE3C COMP2003J COM398 COMP51915 Simulator FIN42330 8PRO102 PGD ELEC5160 XJCO1921 COMP5123M INFS 2042 CHE1148H ECA5313 IN3329 Microsoft Modeling B15 2TT COMP3687 COMP2432 DMS2030 KAN4TSF CFKG CSE 5322 CSE 445 STATS 3DA3 CHC5223 COMP1048 EL3105 EECS 3221 CS231 ME 4434 ITP4510 ITP4501 CSCI 5708 Controller EECE 5699 CPEN 231L CPN programs CSC8016 MATU9D2 ENGN4213 2. SVM. (10 points) Consider an SVM classifier using RBF kernel, and finish the following python code to search for the optimum kernel parameter γ. You are required to calculate the RBF function and search yourself, instead of using functions in Scikit-Learn (sklearn). def parameter search(gamma list ,Xtr,ytr,Xts,yts ,...): """ Parameters gamma list: A list of RBF kernel parameter gamma to search for Xtr: Training data, shape (ntr,nrow,ncol) ytr: Training labels, shape (ntr,) Xts: Test data, shape (nts,nrow,ncol) yts: Test labels, shape (nts,) ... Add other parameters as needed Returns: gamma optimum: The gamma with minimum error on Xts, yts """ ... return gamma optimum
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