Activity Recognition from

User-Annotated

Acceleration Data

Ling Bao and Stephen S. Intille

Presented by: Hong Lu

Key Questions

• Can low cost wearable sensors be used for robust, real- time recognition of activity?

• Can training data be acquired from the end user without researcher supervision?

• Does recognition require user-specific training data?

• Do more sensors improve recognition?

Data Collection

• 13 ♂ + 7♀ = 20 subjects , age from 17 to 48

• 20 everyday activities

• Subjects unsupervised when generating own training data, both in and outside the lab

• What’s the problem of typical laboratory data? WHY?

Often data in lab is collected from researchers as subjects

Lab environments may restrict activity, simplifying recognition !

Making researchers to label training examples does not scale

Recognition rates highly depended on how data is collected95.6% (laboratory data)

VS66.7% (naturalistic settings)

Data Collection

• What’s an accelerometer ?

• An accelerometer is a device that measures the vibration, or acceleration of motion of a structure.

Why Accelerometer ?

• Many daily activities involve repetitive physical motion of the body or specific postures• E.g. Walking, Running, Scrubbing, Vacuuming

• Low cost, tiny, energy efficient • Watch

• Phone, mp3 player

• Camera

• computer

• Game controller, the wii remote

Sensor Placement

• 5 wireless sensors

Right hip

Wrist

upper arm

Ankle

Thigh

• Shack to synchronize

Raw Data

Features

• Why we need them ?

• Summarize the data bin

• Capture useful information

• What is the desired characteristics of a good feature ?

• removing irrelevant noise

• keeping relevant attributes to tell the difference

• easy to compute

?

Features

• 512 sample windows (6.7s ?), 50% window overlap

• Features: • Mean

• Energy

• Frequency-domain entropy

• Correlation Between x, y accelerometer axes each board Between all pair wise combinations of axes on different boards

Classifiers

• Tested on decision table, nearest neighbor ( IBL), C4.5 decision tree, and naïve Bayesian classifiers

• Machine Learning Toolkit (Witten & Frank, 1999)

Training

• Method 1: User-specific training

• Train on activity sequence data for each subject

• Test on obstacle course data for that subject

• Method 2: Leave-one-subject out training

• Train on activity sequence and activity data for all subjects but one

• Test on obstacle course data for left out subject

• Average for all 20 subjects

Results

• C45 Decision tree wins

• It shows

• User-specific training: 71.6 ±7.4

• Leave-one-subject-out training: 84.3 ±5.2

• Why?

• Commonalities between people may be more significant than individual variations

• Larger training set

Result

• Overall, promising • Data collected by subjects themselves without

supervision • Data collected both in and outside of laboratory

setting

• Poorer performance results when… • Activities involve less physically characteristic

movements , Activities involve little motion or standing still

• Activities involve similar posture/movement (e.g. watching TV, sitting and relaxing)

The dark side

• The more sensors you placed, the higher accuracy you may achieved, but …

• cost

• you look weird

• hard to deploy

• more computational horse power

Accelerometer Discriminatory Power

• Tested C4.5 classifier with using subsets of accelerometers:

• Hip, wrist, arm, ankle, thigh, thigh and wrist, hip and wrist

• Best single performers:

• Thigh (-29.5%)

• Hip (-34.1%)

• Ankle(-37%)

Accelerometer Discriminatory Power

• With only two accelerometers get good performance:

• Thigh and wrist (-3.3% compared with all 5)

• Hip and wrist (-4.8% compared with all 5)

Overview

• The study • Activity recognition: 20 household activities

• Sensors: 5 non-wired accelerometers

• Data: participants labeled own data

• Result • Good performance with decision tree classifier

• Subject-specific training data for some activities may not be required

• Reasonable accuracy can be achieved with only 2 of 5 accelerometers

Thank you!

The End

For some slides, I used content of Emmanuel MunguiaTapia’s presentation