1. Power Consuming Activity Recognition in Home Environment
Edinburgh Napier University
Xiaodong Liu and Qi Liu

2. Contents 1 2 3 4 5 Background Related Work Research Methods and Ideas
Experiment Results 5
Conclusion and Future Work

3. Background
Electric energy is one of the most widely used energy sources in mankind, and the energy consumption of residents has become the second largest energy-consuming sector after the industrial sector.
Some objective factors such as weak consumer awareness, old household electricity facilities and obsolete energy-saving technology, resulting in a serious waste of electricity and energy.

4. Background Power Consuming Activity Recognition
Operation and Management of Electric Industry
Energy Saving and Emission Reduction
Development of Smart Home
Users' Energy Consumption Behavior Change

5. Activity recognition system
Related Work
Activity recognition system
2. Wearable devices
1.Camera
Intrusive, privacy, installation cost
Must carry, battery, detection of simple action
Different Approaches to data collection
3. Radio equipment
4. Sensor network
The platform is expensive, only detects simple gestures or actions
Let users perform activities in a comfortable manner

6. Activity recognition based on a Sensor Network
Related Work
Activity recognition based on a Sensor Network
Knowledge-driven：Based on an ontology
Need a clear field of knowledge
The accuracy of knowledge definition determines the accuracy of recognition;
Difficult to adapt to all situations
Activity
Recognition
Data-driven：
Based on probability model
(CRF，HMM)；
Based on data mining
(KNN,SVM，NB)
Need a lot of training processes
No use of domain knowledge

7. Data Collection Hardware
Research Methods and Ideas
Data Collection Hardware
Smart socket
Power metering module: HLW8012 from ShenZhen Heliwei company，
Support 50 / 60Hz IEC 687/1036 standard accuracy requirements.
WIIF module: ESP8266 from Shanghai Lexin company.
Collect information of appliances to recognize activity.

8. Knowledge-driven: Get context location information
Research Methods and Ideas
Knowledge-driven: Get context location information

9. Knowledge of activity ontology
Research Methods and Ideas
Knowledge of activity ontology
rule1
Irrelevant to time point and duration
rule2
Irrelevant to element action order
Activity-Location Dependency:
The logical language is described as:
Activity ∀ hasLocation (Balcony or Bathroom or Bedroom or Diningroom or Kitchen or Sittingroom or OutOfHome)
Activity ∃ hasLocation Locations.
So we generated an
Activity-Location matrix:
rule3
Pra=

10. Research Methods and Ideas
Activity ontology
use "who", "where", "when", “what" to describe power consumption activities, mainly contains person, location, sensor, appliance and activity information dimensions.

11. Data+Knowledge-driven
Research Methods and Ideas
Data+Knowledge-driven
（3）Recursion ，to t=3,4,…, T-1
𝛿 𝑡 𝑖 = 1≤𝑗≤𝑄 𝑚𝑎𝑥 𝛿 𝑡−2 𝑗 𝛿 𝑡−1 𝑘 𝑎 𝑗𝑘𝑖 𝑏 ki 𝑥 𝑡 , 𝑖=1,2,…,𝑄
ψ 𝑡 𝑖 =𝑎𝑟𝑔 1≤𝑗≤𝑄 𝑚𝑎𝑥 𝛿 𝑡−1 𝑗 𝑎 𝑗𝑘𝑖 ,𝑖=1,2,…,𝑄
（4）Terminate ，
𝑃 ∗ = 1≤𝑗≤𝑄 𝑚𝑎𝑥 𝛿 𝑇 𝑖
𝑖 𝑇 ∗ =𝑎𝑟𝑔 1≤𝑖≤𝑄 𝑚𝑎𝑥 𝛿 𝑇 𝑖
（5）Optimal path backtracking ，to t=T-1, T-2,…, 1
𝑖 𝑡 ∗ = ψ 𝑡+1 𝑖 𝑡+1 ∗
Second-order HMM (HMM2) hypothesis:
a) state of time t, not only depend on the state of time t-1, but also depend on the state of time t-2;
b)The observed variable 𝑥 t at time t is related not only to the current state of the system but also to the previous state of the system.
HMM2 Viterbi algorithm ：
（1）Initialize，
𝛿 1 𝑖 = π 𝑖 𝑏 𝑖 𝑥 1 , 𝑖=1,2,…,𝑄
ψ 1 𝑖 =0,𝑖=1,2,…,𝑄
（2）t=2，
𝛿 2 𝑖 = 1≤𝑗≤𝑄 𝑚𝑎𝑥 𝛿 𝑡−1 𝑗 𝑎 𝑗𝑖 𝑏 𝑗𝑖 𝑥 2 , 𝑖=1,2,…,𝑄
ψ 2 𝑖 =𝑎𝑟𝑔 1≤𝑗≤𝑄 𝑚𝑎𝑥 𝛿 𝑡−1 𝑗 𝑎 𝑗𝑖 ,𝑖=1,2,…,𝑄
HMM2K Viterbi algorithm：
Join the Activity-Location matrix Pra and user’s Loc, Search space is not S={1,2,…,Q}，the search space is S’=S.*Pra(Loc(n),:)

12. Data-driven method: HMM2
Experiment Results
Data-driven method: HMM2
Precision= 1 𝐶 𝑖=1 𝐶 𝑇𝑃 𝑖𝑖 𝑁𝐼 𝑖
Recall= 1 𝐶 𝑖=1 𝐶 𝑇𝑃 𝑖𝑖 𝑁𝑇 𝑖
The experiment was first performed on the smart home activity data set established by Kasteren et al. To detect the effectiveness of the second order HMM, which compared the Naive Bayesian (NB) and the first order HMM.
Evaluation parameters
Accuracy= 𝑖=1 𝑄 𝑇𝑃 𝑖𝑖 𝑇𝑜𝑡𝑎𝑙
F1= 2∗𝑝𝑟𝑒𝑐𝑖𝑠𝑖𝑜𝑛∗𝑟𝑒𝑐𝑎𝑙𝑙 𝑝𝑟𝑒𝑐𝑖𝑠𝑖𝑜𝑛+𝑟𝑒𝑐𝑎𝑙𝑙
Precision
Recall
F-measure
Accuracy NB
40.9
38.9
39.8
67.9
HMM
48.3
63.1
54.7
81.0
HMM2
50.2
64.8
56.6
82.1
Analysis: HMM2’s performance is better than NB and HMM1, overall recognition accuracy compared to HMM1 have improved.

13. Data- and Knowledge-driven method
Experiment Results
Data- and Knowledge-driven method
Experiments on our own data set, we collected data of 7 days, activities recorded by self-recording way.
Precision
Recall
F-measure
Accuracy
Time NB
55.1
59.2
57.7
76.4
0.11
HMM
69.3
70.3
69.7
85.3
0.39
HMM2
70.4
71.8
71.1
88.0
0.51
Proposed method
79.9
84.2
82.0
95.8
0.27
Analysis: The static data-driven method is faster, but the accuracy is low, proposed method’s accuracy is better than other methods, and the algorithm cost is much shorter than HMM2.

14. Conclusion and Future Work
In this paper, we use the low-order probability model to recognize the activity. Whether the n-order probability model will increase the accuracy is the research direction of our next stage.
Mining more domain and prior knowledge, single-user and multi-user behavior to help us better sort and combine these activities, so as to more accurately recognize these activities.

15. Thanks!