An Identification Method of IR Signals to Collect Control Logs of Home Appliances

〇Yuta Takahashi1，Teruhiro Mizumoto1

1. Nara Institute of Science and Technology

2017 ACIS Conference Series BCD

July 11, 2017

Background & Motivation

❖Control logs of home appliance

2

❖More intelligent smart home

Log

18:00

Cold

24℃

- ON/OFF

- Channel

- Volume

- Temperature …

Home which can understand user’s preference

- Automation

- Energy efficient

- RecommendationSmart home

Goal

Method for collecting control logs

❖Information appliance

3

❖Estimation by electric consumption

〇Accurate logs

〇 Remote control

Products are not diverse

〇 Compatible with various products

Need for attachments (smart mater)

Difficult to estimate detail usage

IR signal & Problems

❖Collecting IR signals

4

❖Problems of identification▪ Many protocols (NEC, AEHA…)

▪ Repeater functions

▪ Environmental noise

- Various appliances are controlled by IR

- Installing IR receiver to each room

Difficult to identifying

Proposed method

❖Process of IR signal

5

IR remote controller

Preprocess

Comparison

IR Database

Identification of

appliance type

Identification of

command type

Unknown signal

No match

Command type

Appliance type

IR receiver

Identifying by

statistical model

Preprosess

❖Raw IR signal▪ Consist of high/low pulses (PWM, Pulse Width Modulation)

▪ High memory-capacity for devices

▪ High computation for identifying

6

Raw IRPulse width

sequence

❖Pulse width sequence▪ Consist of length of

high/low pulses

▪ Range is 0 to 255

▪ Easy to handle

▪ Low memory-capacity

Comparison method of two signals

7

Two signals

𝑆10

𝑆11

𝑆12

𝑆13

𝑆14

𝑆15

𝑆16

𝑆17

𝑆10

𝑆11

𝑆12

𝑆11

𝑆12

𝑆13

𝑆12

𝑆13

𝑆14

𝑆1

𝑆𝑠𝑢𝑏

𝑆2 𝑆20

𝑆21

𝑆22

𝑆20

𝑆21

𝑆22

𝑆20

𝑆21

𝑆22

𝑀𝐴𝐸0, 𝑆𝐴𝐸0 𝑀𝐴𝐸1, 𝑆𝐴𝐸1 𝑀𝐴𝐸2, 𝑆𝐴𝐸2

𝑝 = argmin(𝑀𝐴𝐸𝑛) 𝑴𝑨𝑬𝒑, 𝑺𝑨𝑬𝒑

A captured signal

A referenced signal

𝑀𝑒𝑎𝑛 𝐴𝑏𝑢𝑠𝑜𝑙𝑢𝑡𝑒 𝐸𝑟𝑟𝑜𝑟 =1

𝑁

𝑖=0

𝑁

|𝑆𝑠𝑢𝑏𝑖

− 𝑆2𝑖|

Sum 𝐴𝑏𝑢𝑠𝑜𝑙𝑢𝑡𝑒 𝐸𝑟𝑟𝑜𝑟 = σ𝑖=0𝑁 |𝑆𝑠𝑢𝑏

𝑖− 𝑆2

𝑖|

(long)

(short)

Dataset

8

14 appliances

↓

140 commands

10 signals

↓

1,400 signals

irMagician1400

2= 979,300 combinations

Error frequency of same appliance and other appliance

9

Same appliance (any command) :

Other appliance (any command) :

A appliance

A1 command

A appliance

A2 command

A appliance

A1 command

B appliance

B1 command

Small

overlapped

Difficult to

fit a model

(over fitting)

Constructing a model of “same appliance” of MAE

Model for identifying appliance type

❖Fitting

▪ Inverse gaussian, Gamma, Inverse gamma, Weibull, Chi and F distributions

▪ Maximum likelihood estimation

▪ AIC (Akaike's Information Criterion)

▪ Inverse gamma (k=3) and F (k=4) are best fitting

10

❖Decision

▪ 95% confidence interval

▪ 𝑒 ≤ 𝑒𝑡ℎ : same appliance

▪ 𝑒 > 𝑒𝑡ℎ : other appliance

Bad fitting (Weibull) Inverse gamma

95% 5%

3.72

𝑒𝑡ℎ

Error frequency of same command and other command

11

Same command (same appliance) :

Other command (same appliance) :

A appliance

A1 command

A appliance

A1 command

A appliance

A1 command

A appliance

A2 command

Good shape

of histogram

Constructing a model of “same & other command” of SAE

Model for identifying command type

❖Fitting

▪ Inverse gaussian, Inverse gamma and F are better than other

▪We chose Inverse gamma as well as model of appliance type

12

❖Decision

▪Bayes’ decision

𝑙𝑜𝑔𝑝 𝑦 = "same"|𝑥

𝑝 𝑦 = "other" 𝑥

▪ Positive : same command

▪Negative : other command

Evaluations

1. Accuracy of identifying appliance type▪ Verifying by 10-fold cross validation

2. Accuracy of identifying command type

▪ Verifying by 10-fold cross validation

3. Simple simulation

▪ Identification depends on signals in database

▪ Constructing database randomly

▪ Check how many signals are needed in database

13

Identification accuracy

14

❖Accuracy of appliance type (total support : 199,778)

❖Accuracy of command type (total support : 12,636)

Result of simple simulation

▪ Simulating 1,400 signals in each number of appliances

▪Correct match rate is stable if 6 signals, or more, are included in the database

15

Stable

Conclusions

❖Proposed method for identifying IR signal by statistical model

❖Identifying appliance accuracy is 95.5%

❖Identifying command accuracy is 92.0%

❖Identification stability is achieved when 6 signals, or more, of

each appliance are included in database

❖We plan to collect and identify the IR signals in real environment

16

Simple simulation

Process

1. Construct database from signals of each appliance

2. Identifying the test signals

3. Increment the number of signals in database

17

❖Matching method

▪ One appliance type most identified is selected

▪ No match : Several types are estimated or no types of identification

Signal:TV

Signal:TV

Signal:TV

Signal:Fan

→ TV

→ TV

→ Fan

Signals identified as same appliance

TV

Test

Compared signals

Labeled appliance & command to signals

Database

Result