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People. Tones: a system for the detection and notification of buddy proximity on mobile People. Tones: a system for the detection and notification of buddy proximity on mobile phones Kevin A. Li Timothy Sohn Steven Huang William G. Griswold University of California, San Diego

ubiquitous ample computational power a few sensors a few actuators proactive context-awareness ubiquitous ample computational power a few sensors a few actuators proactive context-awareness

messaging location-based reminders You are driving by Safeway. Reminder: Buy steak. messaging location-based reminders You are driving by Safeway. Reminder: Buy steak.

unobtrusive notifications The slopes on Beaver Bzzzzt! Run have opened! unobtrusive notifications The slopes on Beaver Bzzzzt! Run have opened!

crappy sensors crappy actuators cheap sensors could lead to many false notifications cheap actuators crappy sensors crappy actuators cheap sensors could lead to many false notifications cheap actuators could lead to misunderstood cues proactive notification + commodity hardware flood of meaningless notifications

People. Tones two proximity states: far and near (< 2 city blocks) when a People. Tones two proximity states: far and near (< 2 city blocks) when a buddy becomes near, play her sound or vibration cue runs on commodity hardware (Windows Smartphone)

“It was so cool to see who was home by the time I got “It was so cool to see who was home by the time I got home. I could tell if Jenny was home when I passed by University. So if we were going to go eat or something I could ask her. Oh she’s home, so let’s call her and see if she wants to eat. ”

approach focus on application goals to avoid overengineered, impractical solution proximity is easier than approach focus on application goals to avoid overengineered, impractical solution proximity is easier than location acceptable if notifications missed or not understood

contributions privacy-friendly proximity detection algorithm technique for reducing sensor noise without sapping power method contributions privacy-friendly proximity detection algorithm technique for reducing sensor noise without sapping power method for generating a language of understandable vibrotactile cues exploratory study of buddy proximity cues

proximity detection proximity detection

build on location? GPS doesn’t work indoors, in urban canyons tower-based systems must keep build on location? GPS doesn’t work indoors, in urban canyons tower-based systems must keep database current require wardriving how about tower overlap? Near. Me [Krumm 2004] [La. Marca et al. 2005] i. Phone

initial data collection used a GSM phone to record the cell towers it saw initial data collection used a GSM phone to record the cell towers it saw every 5 minutes 3 GSM phones, kept 1 stationary gather data at a variety of distances (0 - 1. 2 miles)

initial measurements a and b are the sets of cell towers seen by each initial measurements a and b are the sets of cell towers seen by each phone

initial measurements a and b are the sets of cell towers seen by each initial measurements a and b are the sets of cell towers seen by each phone

evaluating proximity algorithm can our overlap-ratio algorithm detect proximity accurately enough to support nice evaluating proximity algorithm can our overlap-ratio algorithm detect proximity accurately enough to support nice -to-know information?

requirements cannot be annoying when the system detects a buddy is near, they should requirements cannot be annoying when the system detects a buddy is near, they should really be near OK to not detect every time if a buddy is nearby and stationary, we’ll have multiple chances

the dataset [Chen et. al. , 2006] used the dataset collected by wardriving seattle the dataset [Chen et. al. , 2006] used the dataset collected by wardriving seattle

coverage Suburb Downtown coverage Suburb Downtown

metri precision and rec precision 100% precision every report is valid recall 100% recall metri precision and rec precision 100% precision every report is valid recall 100% recall every near incident is detected

how do we extract the relevant data? only care about when two phones are how do we extract the relevant data? only care about when two phones are near or far from each other why not pull out each set of data by different distance thresholds? turns out mobile phone tower readings fluctuate over time (e. g. , due to load balancing) we can crosscut the dataset to approximate precision and recall for different scenarios

nearby extract pairs of readings taken within 90 s 569, 264 pairs from Suburb nearby extract pairs of readings taken within 90 s 569, 264 pairs from Suburb 379, 285 pairs from Downtown calculate precision and recall for different ratio threshold values

nearby precision suburb 100% precision every report was valid 100% recall every near incident nearby precision suburb 100% precision every report was valid 100% recall every near incident was detected downtown

nearby precision suburb 100% precision every report was valid 100% recall every near incident nearby precision suburb 100% precision every report was valid 100% recall every near incident was detected downtown

nearby precision suburb 100% precision every report was valid 100% recall every near incident nearby precision suburb 100% precision every report was valid 100% recall every near incident was detected downtown

nearby recall 100% precision every report was valid 100% recall every near incident was nearby recall 100% precision every report was valid 100% recall every near incident was detected

reducing sensor noise reducing sensor noise

initial approaches wait for 2 consecutive-same-readings – Too many false positives wait for 3 initial approaches wait for 2 consecutive-same-readings – Too many false positives wait for 3 consecutive-same-readings – Too much delay

2 -bit-filter (“eventually 3 more”) 2 -bit-filter (“eventually 3 more”)

filter evaluation for noise filtering, interested in transitions from far to near and vice-versa filter evaluation for noise filtering, interested in transitions from far to near and vice-versa extract seattle wardrive readings at 30 s intervals try three algorithms on this subset, baseline is single report

filter evaluation Filter False Positive Reduction 1 -same (baseline) 0% 2 -same-filter 53. 8% filter evaluation Filter False Positive Reduction 1 -same (baseline) 0% 2 -same-filter 53. 8% 3 -same-filter 80. 9% 2 -bit-filter 84. 9%

adaptive sampling rate sampling once every 20 s kills the phone in less than adaptive sampling rate sampling once every 20 s kills the phone in less than a day increasing sampling rate to once per 90 s helps but introduces a worst-case delay of 270 s sample at 90 s when in steady state, 20 s when transitioning

buddy cues buddy cues

mapping music to vibrations mapping music to vibrations

problem we want to convert music to vibrations… …but the phone’s vibrator only turns problem we want to convert music to vibrations… …but the phone’s vibrator only turns on and off …at single frequency, single amplitude

pulse width modulation electric motors do this to save power in the case of pulse width modulation electric motors do this to save power in the case of vibrotactile motors this also decreases its rotational frequency perceived as different vibration levels can produce 10 levels of 20 ms pulses

capturing the essence of music capturing the essence of music

overview of approach just using beat doesn’t always work mapping lyrics doesn’t work well overview of approach just using beat doesn’t always work mapping lyrics doesn’t work well basic idea: convey the current energy level of the music

remove noise isolate 6. 6 k. Hz to 17. 6 k. Hz components using remove noise isolate 6. 6 k. Hz to 17. 6 k. Hz components using 8 th order Butterworth Filter use amplitude threshold, to keep only components greater than the average

take running sum of absolute value, generate 1 value every 20 ms this keeps take running sum of absolute value, generate 1 value every 20 ms this keeps length consistent

exaggerate features compose output from previous step with power function: Axn , x is exaggerate features compose output from previous step with power function: Axn , x is sample, A and n are constants, 10<=A<15, 1<=n<=2

examples (requires imagination) Beethoven’s 5 th Symphony matching vibration sequence Michael Jackson – Smooth examples (requires imagination) Beethoven’s 5 th Symphony matching vibration sequence Michael Jackson – Smooth Criminal matching vibration sequence

so far… so far…

would the techniques we used for proximity detection, sensor noise filtering and vibrotactile cues would the techniques we used for proximity detection, sensor noise filtering and vibrotactile cues work in the wild? can peripheral cues be deployed on mobile phones despite poor sensors and actuators? (what experiences can such a system enable? ) field study

People. Tones two proximity states, far and near (< 2 blocks) when a buddy People. Tones two proximity states, far and near (< 2 blocks) when a buddy is near, play their song if phone is in vibrate mode, play a matching vibrotactile sequence

participants 3 groups of friends, 2 weeks participants 3 groups of friends, 2 weeks

could you tell who it was? could you tell who it was?

could you tell who it was? could you tell who it was?

user response to the cue user response to the cue

designing peripheral cues for the wild higher comprehension rate when users select their own designing peripheral cues for the wild higher comprehension rate when users select their own cues obtrusiveness of music cues was not a concern mapping music to vibration was most successful for people who knew the songs well semantic association is key to learnability

user experience user experience

“One time at the library, I wanted to eat with someone and so I “One time at the library, I wanted to eat with someone and so I went outside to call someone. The phone vibrated. I just called the person to meet up. ”

lessons cues were sensible, but not socially obtrusive proximity algorithm worked well in the lessons cues were sensible, but not socially obtrusive proximity algorithm worked well in the wild emphasizing elimination of false positives was effective in combination with 2 -bit counter dwelling/lingering led to successful recall

whole can be greater than its parts despite crappy sensors and actuators, mobile phones whole can be greater than its parts despite crappy sensors and actuators, mobile phones can achieve adequate context awareness and notification with careful system-level design these can be brought together into a useful proactive context-aware application like People. Tones

People. Tones: a system for the detection and notification of buddy proximity on mobile People. Tones: a system for the detection and notification of buddy proximity on mobile phones Kevin A. Li Timothy Sohn Steven Huang William G. Griswold University of California, San Diego

EXTRA SLIDES EXTRA SLIDES

measuring vibrations measuring vibrations

zzz vibrotactile signals z z z zzz vibrotactile signals z z z

far apart unfortunately, there are few points in dataset that are both far apart far apart unfortunately, there are few points in dataset that are both far apart and proximate in time expected atypical results used the entire dataset 55, 181, 015 pairs from Suburb 36, 769, 390 pairs from Downtown

far apart precision suburb 100% precision every report was valid 100% recall every near far apart precision suburb 100% precision every report was valid 100% recall every near incident was detected downtown

far apart precision suburb 100% precision every report was valid 100% recall every near far apart precision suburb 100% precision every report was valid 100% recall every near incident was detected downtown

far apart precision suburb 100% precision every report was valid 100% recall every near far apart precision suburb 100% precision every report was valid 100% recall every near incident was detected downtown

far apart recall 100% precision every report was valid 100% recall every near incident far apart recall 100% precision every report was valid 100% recall every near incident was detected

“It was so cool to see who was home by the time I got “It was so cool to see who was home by the time I got home. I could tell if Jenny was home when I passed by University. So if we were going to go eat or something I could ask her. Oh she’s home, so let’s call her and see if she wants to eat. ”

“One time at the library, I wanted to eat with someone and so I “One time at the library, I wanted to eat with someone and so I went outside to call someone. The phone vibrated. I just called the person to meet up. ”

generating vibrations generating vibrations

same location Extract pairs of readings taken within 5 s 28, 625 pairs from same location Extract pairs of readings taken within 5 s 28, 625 pairs from Suburb 19, 087 pairs from Downtown GPS confirmed 99. 9% within 100 m of each other

same location recall 100% precision => every report was valid 100% recall => every same location recall 100% precision => every report was valid 100% recall => every near incident was detected

eyes-free notifications You are driving by Safeway. Reminder: Buy steak. eyes-free notifications You are driving by Safeway. Reminder: Buy steak.

build on location? GPS doesn’t work indoors, in urban canyons tower-based systems must keep build on location? GPS doesn’t work indoors, in urban canyons tower-based systems must keep database current tower adaptation (e. g. , load balancing) [La. Marca et al. 2005]

location sensing on iphone location sensing on iphone

“It was so cool to see who was home by the time I got “It was so cool to see who was home by the time I got home. I could tell if Jenny was home when I passed by University. So if we were going to go eat or something I could ask her. Oh she’s home, so let’s call her and see if she wants to eat. ”

2 -bit-filter 2 -bit-filter