Applications: Activity Sensing Spencer Kaiser, Laurel Khaleel, and Jake Drew Ubiquitous Computing Southern Methodist University
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Researcher or Criminal ? Criminal Researcher Ted Bundy Boštjan Kaluža
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Researcher or Criminal ? Criminal Researcher Frank Abagnale Radoje Milic
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Researcher or Criminal ? Researcher Criminal Matjaž Gams Sydney Biddle Barrows
Ensembles of Multiple Sensors for Human Energy Expenditure Estimation Authors: Matjaž Gams Mitja Luštrek Hristijan Gjoreski Boštjan Kaluža Radoje Milic Sports faculty interested in measuring expenditure for athletes…
Research Overview • Human Energy Expenditure (EE) directly reflects the level of physical activity. • Actual EE is unpractical to measure outside of the laboratory. • Find better ways to estimate EE measuring physical activity with various accelerometers and sensors. • Previous regression models were created by activity. • This research uses an ensemble of regression models for EE estimation which are trained using the output of multiple sensors.
Question How might sports faculty affordances for wearable sensor types and the number of sensors worn be different from other individuals researching energy expenditure or activity sensing?
Energy Expenditure Calorimetry derives the heat transfer associated with changes of its state due for example to chemical reactions, physical changes, or phase transitions under specified constraints. Calorimetry is typically performed with a calorimeter. Direct Calorimetry measures the total heat output of a person. (Not practical outside of the laboratory) Indirect Calorimetry analyzes respiratory gasses which requires a breathing mask. Another approach uses doubly labeled water with marked isotopes which are used to trace the water’s movement throughout the body. (less accurate, but more convenient) http://en.wikipedia.org/wiki/Calorimetry
Cardiopulmonary and Metabolic Testing Metabolic systems measure breath by breath or mixing chamber oxygen consumption, carbon dioxide production, minute ventilation, anaerobic threshold detection, flow volume loops, lung subdivisions, and maximal voluntary ventilation. http://www.urmc.rochester.edu/physiology-exercise-lab/equipment/cardio-testing.cfm
Maximal Oxygen Uptake (VO2Max) • Measures the maximum rate of oxygen consumption. It is used to measure the functional capacity of the heart, which is a strong indicator of cardiorespiratory fitness. • Determined by measuring oxygen and carbon dioxide content in inhaled/exhaled air while continually increasing the intensity of an exercise. • Occurs at the point at which oxygen consumption no longer increases despite additional increases to intensity. http://www.urmc.rochester.edu/physiology-exercise-lab/equipment/cardio-testing.cfm
Resting Energy Expenditure (REE) Resting Energy Expenditure (REE) is the number of calories burned at rest per day. REE provides the total caloric expenditure in 24 hours and is calculated from the gas exchange data collected by the VIAYSIS VMAX metabolic system. http://www.urmc.rochester.edu/physiology-exercise-lab/equipment/cardio-testing.cfm
Pulmonary Function Tests (PFT) Spirometric pulmonary function tests include tests such as forced vital capacity (FVC), forced expiratory volume in one second (FEV1), flow volume loops, and maximal voluntary ventilation (MVV). http://www.urmc.rochester.edu/physiology-exercise-lab/equipment/cardio-testing.cfm
Six Minute Walk Test (6MWT) VO2max can be estimated from 6MWT results using multivariate equations. This test can be performed by elderly and patients who can not be tested with standard treadmill exercise equipment. http://www.urmc.rochester.edu/physiology-exercise-lab/equipment/cardio-testing.cfm
Quark CPET • State-of-the-art breath by breath gas exchange data analysis (VO2, VCO2) • Fast response paramagnetic O2 sensor • Optional 7-liter mixing chamber (either for low or high ventilation ranges) • Fully integrated 12-lead ECG for Stress Testing (option) • Nutritional assessment with face mask or optional canopy hood • Full spirometry and optional exercise SpO2 monitoring http://www.cosmedusa.com/en/products/cardio-pulmonary-exercise-testing/quark-cpet-stationary-cpet
Actigraphy Actigraphy is a non-invasive method of monitoring human rest/activity cycles. A small actigraph unit, also called an actimetry sensor,[1] is worn by a patient to measure gross motor activity. The data can be later read to a computer and analyzed offline. In some applications, such as the Fitbit, the data is transmitted and analyzed in real time Moderately accurate and cost is an issue. FitBit $99.00
Experiment Sensors Shimmer three-axis accelerometer Zephyr BioHarness sensor Cosmed indirect calorimeter BodyMedia SenseWear sensor Baseline EE Current State of the Art Benchmark (METs)
Question Why do these sensors limit the application of their results only to athletes and people highly into the quantified self movement? How they could have made the experiments generalizable to a wider audience?
Sensor Features
Activities Measured By Cosmed Indirect Calorimeter
Features for Training Ensemble Models • Activity (A) – Class label that was being predicted, including a MET value for training. • Acceleration Peaks (AP) – Count of changes in the direction of acceleration numbers • Heart Rate (HR) – Discretized raw sensor data • Breath Rate (BR) - Discretized raw sensor data • Chest Skin Temperature (CST) - Discretized raw sensor data • Galvanic Skin Response (GSR) - Discretized raw sensor data • Arm Skin Temperature (AST) - Discretized raw sensor data • Ambient Temperature (AT) - Discretized raw sensor data Called “Context Components” Values are divided into similar sized bins.
Creating Model Ensembles • 1,000,000 Raw Data Samples Per Volunteer • Four Machine learning methods were tested: linear regression, Gaussian processes, multilayer perceptron (artificial neural network) and SMOReg (support vector regression) • Four models were created for each of the seven Context Components or Features which were divided into discretized bins each. • 4 Methods * 4 Discretized Bins * 7 Context Components = 112 Total Models! • Each time a prediction was made, 7 models were selected for the prediction based upon the same discretized bins created during training. • Each of the 7 model’s MET predictions were combined (using average, median, or other) to estimate the final EE.
Model Validation • A leave-one-person-out cross-validation technique was used • Models were trained on the data of nine people and tested on the remaining person • Procedure was repeated ten times, for each person • Mean Absolute Percentage Error (MAPE) - The mean absolute error divided by the true value was used for evaluation. • MAPE is the most common metric in the EE estimation domain
Results • To evaluate results, they compared against four standard regression methods: • Linear Regression • Gaussian Processes • SMOReg (support vector regression) • Multilayer Perceptron (artificial neural network) • The Ensemble significantly outperformed the baseline and the SenseWear across all four regression methods
Results • The authors also chose to demonstrate results on a context basis • Overall, the MAPE of the Ensemble across all contexts greatly out-performed the MAPE of individual contexts • “The whole is greater than the sum of its parts.” - Aristotle
Results • Lastly, the authors compared estimated MET values against true MET values for a variety of activities and placed the values on a scatterplot • The results indicate that the Ensemble’s estimations better match the actual Cosmed MET values than those of the other two approaches • The SenseWear is intended to be used for “dynamic activities” which is likely why it performed better for those activities and worse for “day -to- day” activities
Discussion Questions • The results in this paper seem pretty straight-forward to me. More sensors and better algorithms mean that the estimation will be better. Is that conclusion common sense? Is writing a paper to prove that valuable to ubicomp research?
Discussion Questions • Is it practical to include this many sensors to measure EE?
Dog’s Life: Wearable Activity Recognition for Dogs Cassim Emma Hughs Thomas Patrick Oliver Nils Hammerla Ladha Ploetz
Brief Overview • “communicative behaviors” – specific moods, desires, or intentions of the animals • Comprised of movements that demonstrate the body language of the dog • “response behaviors” – last for a short period of time and are usually a response to stimuli • Certain behaviors have been shown to be indicative of disease and pain • A dog’s head plays a key role in most of its normal activities
Activity Sensing Platform • Using a wearable accelerometry sensing platform • Platform: • AX3 accelerometer – Axtivity • 3 axis accelerometer • Contains an accelerometer and microcontroller • Sensor attached to a dog collar • Records a continuous stream of data
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