This document presents a study on using vibration sensors and machine learning methods for occupancy detection. It discusses current energy issues in buildings and the need for an occupancy detection system. It describes using vibration sensors as an alternative to other sensor types. The study uses two wireless accelerometers to collect vibration data from a hallway and classroom as people walk by. Features are extracted from the data and a neural network is used to classify the number of occupants. The neural network model achieves over 90% accuracy in detecting 1-6 occupants. The study concludes neural networks provide the best results for occupancy detection compared to other machine learning models.

1. ODVSML: Occupancy Detection using
Vibration Sensors and Machine
Learning Methods
SHOUNAK MITRA1, TAT FU1, NICHOLAS KIRSCH2
1CIVIL & ENVIRONMENTAL ENGINEERING, UNIVERSITY OF NEW HAMPSHIRE, DURHAM, NH, USA
2ELECTRICAL & COMPUTER ENGINEERING, UNIVERSITY OF NEW HAMPSHIRE, DURHAM, NH,
USA
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2. Overview
 Current Energy Issues and Need for occupancy detection system
 Current Systems and their Drawbacks
 Case Study and Proposed System
 Results
 Conclusion
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3. Current Energy Issues and Need for occupancy detection
system
 Total building primary energy consumption in 2009 was about 48% higher than
consumption in 1980.
 Space heating, space cooling, and lighting were the dominant end uses in
2010, accounting for close to half of all energy consumed in the buildings
sector.
 The U.S. buildings sector alone accounted for 7% of global primary energy
consumption in 2010.
 A key component in building control is the ability to detect occupancy. Most
buildings are conditioned on fixed schedules, e.g., from early morning until late
evening during weekdays.
 However, this leads to a significant waste in energy consumption, because the
HVAC schedule does not consider the actual real-time local conditioning needs
of the building.
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4. Sensors and their Drawbacks 4
Radio Frequency Identification Infrared
CO2 sensors Camera

5. Need of the hour!
 Goal:
• Tracking individuals once they have entered a building without infringing on
their concerns for privacy
• tracking is nondescript
• Tracking an entity and not identify the person.
 Why vibration sensors?
• Alternate use for Structural Health monitoring
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6. Case Study
Cases
Goal : Classify 1-6 Persons walking
(Completed Study)
Goal: a. Classify if the room is occupied or not.
b. Classify number of occupants in the
room (Current undergoing study)
Algorithms
Used
Neural Network SVM EM
6Hallway Classroom

7. Development of Inventory
No. of Persons walking
(A)
No of samples per combination
(B)
No of events captured
6 𝐶(𝐴) ×(𝐵)
1 6 36
2 4 60
3 4 80
4 4 80
5 8 48
6 10 10
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 Two wireless 16-bit accelerometers were used
 The data was aggregated using a wireless data aggregator with an Ethernet
interface with a 2.4 GHz antenna and 9VDC power supply.
 Matlab, JMP Pro used for data processing
No. of Participants (p) = 6

8. Schematic Diagram of the working of the system 8
Data
Cleaning
Consider
Data Above
Mean
Event
Detection
Apply δt
Threshold
1P Walking
>1P
Walking
Extract
Features
PCA
Neural
Networks
Is δt =
[0.4 0.6]
2P Walking
3P Walking
4P Walking
5P Walking
6P Walking
Input Signal

9. Case – I
Event Detection - Thresholding and noise removal
9
Moving Window RMS Approach

10. Apply δt Threshold & Classification using Machine Learning 10
1P
More
than 2P
2PNN
1 Person
2 Persons
> 2 Persons

11. Statistical Features Used in the Study 11

12. Configuration of Neural Network 12
 A Feed-Forward Neural Network was applied for model
training, testing and validation.
 Back-propagation algorithm was implemented as the
learning algorithm, where the network error is back-
propagated from the output to input layer.
 Random-Holdback was used to separate out the training
and test sets in the ratio of 7:3.
 A neural network of 1 hidden layer with 3 neurons was
implemented, in which the Log Sigmoid transfer
function was used in the hidden layer.
 The learning rate was kept at 0.1. Once the network
was trained with the input data, it was used to predict
the test data.
 Since neural network is a slow learner, the training phase
was repeated 10 times in order to increase the
probability to reach a global solution.
Extracted Features/
Input Layer
Log Sigmoid
Transfer Function
Output

13. Results:
Accuracy
Table
Predicted
Actual 1 2 3 4 5 6 Expected number of
people
1 1 0 0 0 0 0 1
2 0 0.818 0.182 0 0 0 2.182
3 0 0.162 0.727 0.11 0 0 2.94
4 0 0 0.083 0.75 0.083 0.083 4.16
5 0 0 0 0.091 0.727 0.182 5.091
6 0 0 0 0 0.25 0.75 5.75
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Confusion Matrix: Number of people walking

14. Results:
Number of People
walking
Neural Network
(Error %)
K-means Clustering
(Error %)
Partition Modeling
(Error %)
1
0.00 0.00 0.00
2
9.10 16.00 15.00
3
-2.00 8.67 12.33
4
4.00 17.00 16.50
5
1.82 10.00 15.00
6
-4.17 13.83 14.50
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Error table

15. Conclusion
 This study presents a review of machine learning methods applied to
occupancy detection in the case of smart building systems while
maintaining privacy and minimizing training time needed before functional
implementation.
 Among the four models used for the predictive analysis; K-means Clustering,
Partition Modeling, Generalized Linear Modeling, Neural Networks, Neural
Networks give the best results  Owing to the back propagation algorithm.
 The overall accuracy of the system is quite high, although misclassification
may occur given certain parameter changes.
 The ability to detect movement in a hallway, following it into a room while
minimizing the existence of privacy infringing discomforting sensors is of
great benefit to many institutions.
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