1. A Longitudinal Study of Pressure Sensing to Infer Real-World Water Usage Events in the Home
Jon Froehlich, Eric Larson, Elliot Saba, Tim Campbell, Les Atlas, James Fogarty, and Shwetak Patel
Nina Sakhnini

2. Overview
Pressure sensing to infer real-world water usage events in homes
Deployed pressure sensors to monitor pressure transients
Deployed a ground truth sensor network to monitor valve- level water usage by fixtures and appliances
Usages are classified using a probabilistic algorithm that leverages a language model, grammar, and prior probabilities

3. Deployment
Deployed in three homes (H1, H2, H3) and two apartments (A1, A2)
At each site, two pressure sensors were and a set of custom wireless sensors that provided ground truth labels of water usage activity for the pressure stream
The deployments began February 2010 and lasted for five weeks

4. Ground Truth
An array of seven external ground truth sensors was deployed
Tracked when each valve was opened or closed
Categorized temperature into hot only, cold only, and mixed
Components:
A parent sensor board to communicate water usage data
XBee Pro wireless modem to transmit sensor state
Transmits a signal once every four minutes to a laptop installed at each site
Sensors

5. Ground Truth Sensors Faucets:
Reed switches (N=34) that react to the presence of a magnetic field
Accelerometers (N=14) for faucets with a single handle to measure acceleration and interpret the handle’s position and temperature
Hall effect sensors (N=3) that sense the distance between two magnets. used for sensing faucets which control temperature using planar rotation but control flow through an up/down motion
Omni-directional ball switch (N=39) that is a vibration sensor to wake the parent sensor board
Appliances:
Power usage sensors (N=7) Power consumption patterns
Push buttons (N=2) when there was no access to the power, residents push the button when using the machine
Thermistors (N=3) attached to the water drain pipe to measure the temperature

6. Pressure Sensors and setup
Pace Scientific P1600s with a resolution of psi connected to a microcontroller interfaced with Bluetooth
Two sensors to collect data from hot and cold- water access points simultaneously
Communicates data to the laptop
Communicate with the ground truth sensor network
Data uploaded to a backend web server every 30 minutes
The backend send notifications when a sensor was not heard from for 10 minutes
Data archived locally for backup

7. Analysis of the Collected Dataset
2.9% of the data is marked as uncertain and 3.9% are unknown events
Not used in classification but not removed from the dataset
Even proportion of cold and hot events (40.7% for cold only, 39.2% for hot, and 20% for mixed).
Four fixtures (kitchen sink, master bathroom sink and toilet, and secondary bathroom sink) are 84.7% of the events
Analysis of the Collected Dataset

8. Valve Event Classification Algorithm
Classifies pressure transients using a probabilistic approach using Bayesian estimation inspired by models used in speech recognition
Provides robustness against transient deformations that can occur during natural valve usage such as compound and collision events
A compound valve event is a valve event that occurs while another fixture is using water
Result a severe attenuation of the high frequency component of the pressure transient
A collision valve event is a valve event that occurs within two seconds of one or more other valve events
The two colliding transient waveforms become highly distorted
Usually these events they are offset by ms

9. Valve Event Classification Algorithm
Incoming water pressure data stream is buffered, and the pressure transients are segmented using time series boundaries defined by the ground truth annotations
Segmented pressure transients are compared to a library of labeled templates using similarity algorithms
A language model determines the likelihood of a given sequence of valve signatures and links open and close valve events into paired tuples
Features are extracted from these paired tuples and compared with smoothed probability distributions
Probabilities from the previous steps are multiplied for each sequence
The sequence with the highest probability is selected

10. The Language Model
Assigns probabilities for possible valve sequences using bigrams
Bigram analysis is commonly used to examine co-occurrences of words or letters
Bigrams here are groups of two sequential valve events
The language model consists of transition probabilities for every valve pair and is trained by counting the number of co-occurring valve pairs in the library
These counts are smoothed using Katz smoothing that works to assign a non-zero probability to every sequence
Handles transition probabilities between two valves that rarely occur in the library
Transition probabilities are used to select the optimal word (valve) sequence from all possible word (valve) sequences
Maintain an n-best list of sequences and dynamically reorder the most probable sequences as new events occur

11. Grammar
Defines the possible ways in which valve sequences can be constructed
Grammar rules are:
An opening of valve must be followed by a closing of valve
A valve’s closure must be after its opening
The temperature state of a valve must be consistent
Uses a soft grammar which applies a penalty to any valve sequence that violates a rule
Sequences which contain grammatical errors but have the likeliest probabilities from the other terms are selected as correct
The grammar is applied to each sequence in the n-best list

12. This is interesting, but I am not sure I am convinced
This is interesting, but I am not sure I am convinced. Are sequences of valve events equivalent to grammatically correct sentences? I understand that a sequence of valve states can be a unique permutation but can the permutations be extensive enough to form grammar of their own?  --Harish
The use of language processing techniques for analysis of water events provides an interesting application of technology. –Jesse
Grammar
Defines the possible ways in which valve sequences can be constructed
Grammar rules are:
An opening of valve must be followed by a closing of valve
A valve’s closure must be after its opening
The temperature state of a valve must be consistent
Uses a soft grammar which applies a penalty to any valve sequence that violates a rule
Sequences which contain grammatical errors but have the likeliest probabilities from the other terms are selected as correct
The grammar is applied to each sequence in the n-best list

13. Classification Algorithms
Template- and Feature-Based Comparison: compares the segmented unknown pressure transient with open and close valve templates in the library
Template Comparison + The Language Model + The Grammar
Template Comparison + The Language Model + The Grammar + Paired Valve Tuple Priors: pairing valve events to link open and close transients together and to compute classification features, such as water usage duration
Classification Algorithms

14. Classification Baseline performance: chance and a majority classifier
always selects the most likely result based only on frequency
Three level classification to observe performance at different granularities:
Valve level: identify the correct fixture and its temperature
Fixture level: ignores temperature
Fixture category level: classify open/close events as bath, clothes washer, dishwasher, faucet, shower or toilet

15. Results
The best performing algorithm is Template Comparison + The Language Model + The Grammar + Paired Valve Tuple Priors
Average overall classification accuracy of 75.5%, 89.5%, and 95.9% for valve, fixture, and fixture- category level
The most frequently used fixtures:
Kitchen sink
Bathroom sinks
Bathroom toilets
Confusions tend to occur within fixture categories and between fixtures that are situated close together
Regarding to compound and collision events, the language model-based algorithms tend to perform better than the template-based algorithm

16. Results of Results
Three-way repeated measures ANOVA was conducted to test whether Template Comparison + The Language Model + The Grammar + Paired Valve Tuple Priors algorithm improved significantly over the template-based algorithm
Using the10-fold classification accuracies as the dependent variable
Sensing resolution, number of sensors, and algorithm as within- subjects factors
A significant main effect of classification algorithm was found (F(1,4)=21.76, p=.010)

17. Suggestions for Improvement
The current approach trains the language model and priors using data from the home where it is deployed
Suggests the usage patterns and priors across different homes reducing system calibration
An interface to allow correction of misclassifications and training of the algorithm over time

18. Suggestions for Improvement
For the water monitoring project, it will be interesting to see if a one size fits all approach for training data will be sufficient, or if different training data for different environments (home, condo, apartment, high rise, etc.) would be needed to properly filter the noise. – Jesse
I feel that this paper doesn't go to the depth of generalizing sensing to a larger, more interesting, context. – Andrew
The current approach trains the language model and priors using data from the home where it is deployed
Suggests the usage patterns and priors across different homes reducing system calibration
An interface to allow correction of misclassifications and training of the algorithm over time

19. Thank You