Upgrade to PRO for Only $50/Year—Limited-Time Offer! 🔥
Speaker Deck
Features
Speaker Deck
PRO
Sign in
Sign up for free
Search
Search
Shoe Recognition Model with Floor Pressure Sens...
Search
yumulab
November 06, 2024
Research
0
48
Shoe Recognition Model with Floor Pressure Sensors (Slide)
2024年11月3日(日)〜7日(木)に開催されたSENSORCOMM2024の発表資料(スライド)
yumulab
November 06, 2024
Tweet
Share
More Decks by yumulab
See All by yumulab
Bluetooth Low Energyの海に潜る / Dive to Bluetooth Low Energy
yumulab
0
79
湯村研究室の紹介2025 / yumulab2025
yumulab
0
270
Proposal of an Information Delivery Method for Electronic Paper Signage Using Human Mobility as the Communication Medium
yumulab
0
5
Proposal of an Information Delivery Method for Electronic Paper Signage Using Human Mobility as the Communication Medium / ICCE-Asia 2025
yumulab
0
54
研究室から社会へ 〜 情報科学でつなぐ科学技術コミュニケーション実践 / #CoSTEP20th
yumulab
0
70
A Proposal of an Information Delivery Method using Human Movement as a Communication Medium for Electronic Paper Signage / ICEC2025
yumulab
0
22
メタバース空間で対話相⼿に向かって⾃律移動するAIアバター『ノア』の開発 / EC2025-Oyamada
yumulab
0
35
足位置の視覚的提示による電子オルガンのペダル鍵盤演奏学習支援システムの提案 / EC2025-Hokin
yumulab
0
34
電子ペーパーサイネージにおける人の移動を通信媒介とした情報配送手法の提案 / EC2025-Akiba
yumulab
0
26
Other Decks in Research
See All in Research
財務諸表監査のための逐次検定
masakat0
0
210
LLM-jp-3 and beyond: Training Large Language Models
odashi
1
720
SREのためのテレメトリー技術の探究 / Telemetry for SRE
yuukit
13
2.6k
若手研究者が国際会議(例えばIROS)でワークショップを企画するメリットと成功法!
tanichu
0
130
大規模言語モデルにおけるData-Centric AIと合成データの活用 / Data-Centric AI and Synthetic Data in Large Language Models
tsurubee
1
460
EarthDial: Turning Multi-sensory Earth Observations to Interactive Dialogues
satai
3
450
ACL読み会2025: Can Language Models Reason about Individualistic Human Values and Preferences?
yukizenimoto
0
100
AIスパコン「さくらONE」のLLM学習ベンチマークによる性能評価 / SAKURAONE LLM Training Benchmarking
yuukit
2
900
自動運転におけるデータ駆動型AIに対する安全性の考え方 / Safety Engineering for Data-Driven AI in Autonomous Driving Systems
ishikawafyu
0
110
Community Driveプロジェクト(CDPJ)の中間報告
smartfukushilab1
0
100
Combining Deep Learning and Street View Imagery to Map Smallholder Crop Types
satai
3
310
生成AI による論文執筆サポート・ワークショップ ─ サーベイ/リサーチクエスチョン編 / Workshop on AI-Assisted Paper Writing Support: Survey/Research Question Edition
ks91
PRO
0
120
Featured
See All Featured
Helping Users Find Their Own Way: Creating Modern Search Experiences
danielanewman
31
3k
How to Think Like a Performance Engineer
csswizardry
28
2.4k
How to Talk to Developers About Accessibility
jct
1
83
Jamie Indigo - Trashchat’s Guide to Black Boxes: Technical SEO Tactics for LLMs
techseoconnect
PRO
0
29
Lightning Talk: Beautiful Slides for Beginners
inesmontani
PRO
1
400
Measuring & Analyzing Core Web Vitals
bluesmoon
9
710
What’s in a name? Adding method to the madness
productmarketing
PRO
24
3.8k
New Earth Scene 8
popppiees
0
1.2k
SERP Conf. Vienna - Web Accessibility: Optimizing for Inclusivity and SEO
sarafernandez
1
1.3k
Redefining SEO in the New Era of Traffic Generation
szymonslowik
1
160
Efficient Content Optimization with Google Search Console & Apps Script
katarinadahlin
PRO
0
240
How to optimise 3,500 product descriptions for ecommerce in one day using ChatGPT
katarinadahlin
PRO
0
3.4k
Transcript
Shoe Recognition Model with Floor Pressure Sensors Sora Kamimura *1
Tetsuo Yutani *2 Atsuko Shibuya *2 Tsubasa Yumura *1 *1 Hokkaido Information University *2 FirstFourNotes, LLC <%FNP>
Table of contents #BDLHSPVOE1VSQPTF 1SFTTVSFTFOTPS .PEFM
%BUBTFUT 3FTVMU%JTDVTTJPO $PODMVTJPO
#BDLHSPVOE1VSQPTF 5SB ffi D fl PXBOBMZTJT #ZDBNFSB 4VSWFJMMBODFDBNFSBTDBOCFEJWFSUFE $BONBOBHFBMBSHFBSFBCZPOFDBNFSB *OWBTJPOPGQSJWBDZ
#MJOETQPUTDBVTFECZPCTUBDMFT ɾ*NQSPWFEXPSLF ffi DJFODZ ɾ"WPJESJTL DPMMJTJPOT GBMMT FUDʜ #Z fl PPSQSFTTVSFTFOTPS /PQSJWBDZJTTVF /PCMJOETQPUT
#BDLHSPVOE1VSQPTF 5SB ff i D fl PXBOBMZTJTCBTFE fl PPSQSFTTVSFTFOTPS SFRVJSFTJEFOUJ
fi DBUJPOPGQFPQMF )VNBOJEFOUJ fi DBUJPOVTFT XFJHIU TUSJEFMFOHUI TQFFE TIPFUZQF FUD *OUIJTTUVEZ XFEFWFMPQUIF TIPFSFDPHOJUJPONPEFMXJUI fl PPSQSFTTVSFTFOTPST
1SFTTVSFTFOTPS .BUFSJBMT ɾ7FMPTUBU QSFTTVSFTFOTJUJWFDPOEVDUJWFTIFFU ɾ$PQQFSGPJMUBQF ɾ"SEVJOP 4USVDUVSF ɾ$PQQFSGPJMUBQFTBSFNNXJEFBOE ɹTQBDFENNBQBSU ɾUBQFTCPUIWFSUJDBMMZBOEIPSJ[POUBMMZ
ɾNFBTVSFNFOUQPJOUT ɾ.FBTVSFUIFWPMUBHFBUFBDIQPJOUFWFSZNT
.PEFM ɾ5IF*OQVUMBZFSSFDFJWFTSBXEBUB ɾ5IFIJEEFOMBZFSJTUISFFGVMMZDPOOFDUFEMBZFS ɾ5IFPVUQVUMBZFSXJMMPVUQVUUIFTIPFJEFOUJ fi DBUJPOFTUBCMJTINFOU ɹ TOFBLFS SPPNTIPF BOETBOEBM
/FVSBMOFUXPSLNPEFM
%BUBTFUT )PXUPDPMMFDU $BMJCSBUJPOUIFTFOTPS 1VUPOTIPF 8BJUBGFXTFDPOET TPST
.FBTVSFNFOUT .FBTVSFNFOUTUJNFT JOFBDITIPF %BUB5ZQF ɾ&BDITFOTPS ʙ PS.FSHFE ɾ)PXMPOHXBJUGPSNFBTVSF TPST ɾ/PSNBMPS3PUBUFEEFHSFFTGPS ɹEBUBBVHNFOUBUJPO
3FTVMU ɾ5IF'NFBTVSFJOTJT ɹIJHIFSUIBOT ˠ7JCSBUJPOTBOEPUIFSOPJTFTJT ɹMFTTCZXBJUJOH ɾ*OTFDPOET UIF'NFBTVSFPG ɹNFSHFEEBUBJTIJHIFSUIBO ɹFBDITFOTPSEBUB ˠ5IFTFOTPSIBTBTFOTJUJWJUZCJBT
ɹ"OE NPEFMDBOUMFBSOJUCZ ɹFBDITFOTPSEBUB ɹ#VUMFBSOJOHCFDBNFQPTTJCMFCZ ɹNFSHFEEBUB 'NFBTVSF F = 2 × precision × recall precision + recall
$PODMVTJPO ɾ*OUIJTTUVEZ XFEFWFMPQFEUIFOFVSBMOFUXPSLNPEFMUPSFDPHOJ[F ɹTIPFUZQFTXJUI fl PPSQSFTTVSFTFOTPSXJUIB7FMPTUBU ɾ6TJOHBSPUBUFEEBUBTFUQSPWFEUPCFUIFNPTUF ff FDUJWFBQQSPBDIGPS ɹSFBMXPSMEBQQMJDBUJPOT
ɾ5IFSFBSFMBSHFEJ ff FSFODFCFUXFFOUIFFYQFSJNFOUBMFOWJSPONFOU ɹBOEUIFBTTVNFESFBMFOWJSPONFOU ɾ5IFSFBSFQSPCMFNTTVDIBTSFBDUJPOSBUFBOEBMMPXBCMFQSFTTVSF ɾ8FXJMMDPOUJOVFUPEFWFMPQCPUIIBSEXBSFBOETPGUXBSFUPTPMWF ɹUIFTFQSPCMFNT