The activities of daily living of a patient in a smart home environment can be detected to a large extent by the real-time analysis of characteristics of the habitat's electrical consumption. However, reasoning over the conduct of these activities occurs at a much higher level of abstraction than what the sensors generally produce. In this paper, we leverage the concept of Complex Event Processing (CEP), in which low-level data streams are progressively transformed into higher-level ones, to the task of activity recognition. We show how the use of an appropriate representation for each level of abstraction can greatly simplify the process. We also report on the use of an existing event stream processor to successfully implement the complete chain, from low-level sensor data up to a sequence of discrete and high-level actions.

1. Activity Recognition Through Complex
Event Processing: First Findings
Sylvain Hallé, Sébastien Gaboury
and Bruno Bouchard
Université du Québec à Chicoutimi, Canada
February 12th, 2016

2. IMPOSTOR WARNING

4. 1. ...
2. ...
3. ...

5. 1. ...
2. ...
3. ...
X

6. 1. ...
2. ...
3. ...
X

11. . . .

12. . . .

13. Time
Power
Peak
Plateau

14. 0
200
400
600
800
1000
1200
8744 8745 8746 8747 8748 8749 8750 8751 8752 8753 8754
S.WL2
S.WL1
T.x
Example: Blender

15. SS-PL3>-393
| FFT-L2-6<=-3
| | SS-PL2<=1071: ToasterON
| | SS-PL2>1071: ElectricKettleON
| FFT-L2-6>-3
| | FFT-L2-6<=9
| | | SS-PL1 <=148
| | | | SS-QL1<=-169: RangeHoodFanOFF
| | | | SS-QL1>-169
| | | | | SS-QL1<=135
| | | | | | SS-PL1<=-788: CoffeeMakerOff
| | | | | |
| | | | | | SS-PL1>-788
| | | | | | | FFT-L1-1<=-788:
| | | | | | | CoffeeMakerOFF
| | | | | | | FFT-L1-1>-408
| | | | | | | | FFT-L1-5 <=153:MixerOff
| | | | | | | | FFT-L1-5->153
...
(Maitre et al. 2013)

16. SS-PL3>-393
| FFT-L2-6<=-3
| | SS-PL2<=1071: ToasterON
| | SS-PL2>1071: ElectricKettleON
| FFT-L2-6>-3
| | FFT-L2-6<=9
| | | SS-PL1 <=148
| | | | SS-QL1<=-169: RangeHoodFanOFF
| | | | SS-QL1>-169
| | | | | SS-QL1<=135
| | | | | | SS-PL1<=-788: CoffeeMakerOff
| | | | | |
| | | | | | SS-PL1>-788
| | | | | | | FFT-L1-1<=-788:
| | | | | | | CoffeeMakerOFF
| | | | | | | FFT-L1-1>-408
| | | | | | | | FFT-L1-5 <=153:MixerOff
| | | | | | | | FFT-L1-5->153
...
(Maitre et al. 2013)
How do you isolate a single device?
How do you add a new device?
How do you adjust noise thresholds?
How do you reuse that output?

17. Alan Perlis
(1922-1990)
Beware of of the
Turing tar-pit in
which everything is
possible but nothing
of interest is easy. ,,
,,

18. OI I . . .
BREAKFAST
Signal processing
Finite-state machines
?

19. C
E
P
omplex
vent
rocessing

20. A declarative event stream
query engine

21. Booleans
B
Numbers
R
2
3
4
π
Strings
S
abc
Functions
X Y→
Sets
X
2

22. A processor is a function that takes 0 or more
event traces as input, and returns 0 or 1
event trace as output
. . . . . .

23. Processors can be composed: the output of
a processor can be given as the input of
another ("piping")

24. BeepBeep is an event stream query engine
that provides...
A set of basic processors (independent from
any event type)
A core grammar and interpreter to
compose ("pipe") processors
Mechanisms to extend the grammar
with user-defined events, processors, etc.

25. abc. . . a . . .
n c
Ψ
abc. . . b . . .
n c
EVERY nTH OF (T)
TRIM n FROM (T)

26. WHERE2 1 5 0 2 0
C
C2 1 5 0
(T) WHERE condition

27. COMBINE (T) WITH f
Σ+2 1 5 0 2 3 8
f
f
8
2 1 5 0 2 1 5 0
f1 5 0 1 5 0
f5 0 5 0
++
+

28. a . . .b
a . . .b
FILE "filename"
SAVE (T) TO "filename"

29. On every fifth trading day starting today,
calculate the average closing price of MSFT
for the five most recent trading days, and keep
the query standing for fifty trading days.
timestamp stockSymbol closingPrice
0 APPL 1039.3
0 MSFT 950.0
0 GOGL 433.3
1 MSFT 951.2
1 APPL 1038.3
... ... ...

30. On every fifth trading day starting today,
calculate the average closing price of MSFT
for the five most recent trading days, and keep
the query standing for fifty trading days.
String line = br.readLine().trim();
if (!line.isEmpty()) {
String[] parts = line.split(",");
if (parts[0].compareTo("ABC") != 0) {
value_index++;
sum += Double.parseDouble(parts[1]);
if (value_index == 5) {
double average = sum / 5;
value_index = 0;
sum = 0;
return average;
}}}

31. On every fifth trading day starting today,
calculate the average closing price of MSFT
for the five most recent trading days, and keep
the query standing for fifty trading days.
SELECT afd FROM (
SELECT S1.timestamp AS ts,
AVG(S2.closingPrice) AS afd
FROM
(SELECT * FROM stocks
WHERE stockSymbol = "MSFT") AS S1,
(SELECT * FROM stocks
WHERE stockSymbol = "MSFT") AS S2
WHERE (S2.timestamp - S1.timestamp) < 5
GROUP BY S1.timestamp) AS S3
WHERE MOD(ts, 5) = 0;

32. On every fifth trading day starting today,
calculate the average closing price of MSFT
for the five most recent trading days, and keep
the query standing for fifty trading days.
EVERY 5TH OF (
APPLY (THE AVERAGE OF (*)) TO (
SELECT closingPrice FROM stocks)
WHERE (stockSymbol) = ("MSFT"))))
ON A WINDOW OF 5).

34. *

35. 10*

36. 100
>
10*

37. 100
>
0
10*

38. 100
>
0
0
>
0
10
10*

39. 10
100 10
WL1

40. 700
200
200
I
*
*
O
700
*200
*

41. OI I

46. It works
1500 Hz performance
Rapid prototyping -400 LOC3
What vs. how
https://liflab.github.io/
beepbeep-3