Hybrid Recommender System Architecture for Personalized Wellness Management

HYBRID RECOMMENDER SYSTEM ARCHITECTURE FOR PERSONALIZED WELLNESS

MANAGEMENT

Master of Science – Thesis Defense

Prasad Priyadarshana Fernando, Boosabaduge

Complex Engineered Research LabDepartment of Electrical and Computer Engineering

The University of Akron

02/24/2016

OUTLINE

Introduction Background Hybrid Recommender System Architecture Design of the Hybrid Recommender System Illustrative Examples and Evaluation Conclusion and Future Works Demonstration

Introduction

• The current healthcare costs in the USA exceeds $3.8 trillion and the reactive approach to managing health is not sustainable

• The focus on keeping healthy people healthy is one approach to addressing this serious societal concern

• Exercising is one of the key requirments for a healthy life

Why Exercising is Important

• Helps prevent diseases• Improves stamina• Strengthens and Tones• Enhances flexibility• Controls weight• Improves quality of life

Why Exercise Recommendation is needed

• Don’t know what to do• Don’t know how to do• Consulting an expert is expensive• Consulting an expert is time consuming• Freely available guidelines are not personalized

Our Goal

• To empower wellness management at the scale of the society, efficient software tools are necessary

• So the design objectives for our system were;– To Reduce the cost burden on participants– To Improve the access to expertise for the participants– To explore personalized options for the participant

Challenges in Recommending Exercises

• Recommender Systems are widely used to recommend items such as movies, books, products and services to users

• The problem of recommending exercises to participants in a wellness program is, however challenging and nuanced

• Participants must adhere to the appropriate intensity and frequency to benefit from the exercise

• These exercises must help the participants to achieve their wellness objectives

Why it is different from Item Recommendation

• The efficacy of the exercises recommended is not merely the user preference

• The efficacy must often be assessed after a lapse of several weeks

• Recommended exercises must be refined over a duration of time

• Health, physical, motivational, occupational, etc. conditions of a person tend to change as the particular person uses the recommended exercise

The Basis for the Solution

• The participants are expected to interact regularly with the system by providing their latest measurement so that the software systems can make decisions based on the individual's requirements with the time

• The main concept that was exploited is, the people with similar physical and motivational conditions tend to perform similar kind of exercises

Proposed Solution

• To address the above challenges, we architected a hybrid recommender system that incorporates two major components– Rule Based Expert System : To evaluate the participant’s

conditions in order to come up with a possible exercise schedule

– Collaborative Filtering Recommender System : To Explore more exercises that would be interesing to the participant

OUTLINE


Background

• The background knowledge on the following fields were useful in building our Hybrid Recommender System (HyRES) for Exercise Recommendation;1. Exercise Science Domain Knowledge2. Rule Based Expert System3. Recommender System

Exercise Science

• In order to integrate exercise science domain knowledge into HyRES, we used the following overall strategy;– Determine the participant's capability to exercise based on

physical activities performed recently– Come up with a suitable exercise duration for the participant

based on the fitness status and the goal of exercising– Consider the participant's anthropometric and biometric data to

modify the exercise schedule– Consider the gender, age, diseases and disabilities as high

priorities to alter the recommended intensity and duration– Estimate the desired daily calorie intake and compare it with the

actual calorie intake to change the exercise schedule– Explore more exercise based on participants preferences

Estimating the Ability to Perform Exercises

Evaluating Participants and Exercises

• Metabolic Equivalent of Task (MET) – Exercise Intensity Ratio

• Basal Metabolic Rate (BMR) – Participant’s Daily Calorie Intake

• Body Mass Index (BMI) – Participant’s Obesity

Rule Based Expert System

• The exercise science knowledge and practice guidelines were codified as rules in the Domain Knowledge Module

• We utilized a Forward Chaining system that started with the facts and triggered actions or conclusions based on the Rete Algorithm

• We selected the JBoss Drools platform because it was open source and supported easy integration with the Java implementation of HyRES

Recommender System

• Recommender Systems use two main techniques to recommend new items to users;– Content Based (CB) Recommendation– Collaborative Filtering (CF) Recommendation

• Starting with the output of the Expert System, the Recommender System generates more recommendations by accounting for user preferences

• CF techniques are used in HyRES to find;– Similar exercises to participant’s preferred exercises– Similar exercises that similar participants are preferring

Collaborative Filtering Approach

• In Collaborative Filtering, we considered two main approaches– Neighborhood Method– Latent Factor Model

• The basis for the CF techniques is the user-exercise preference matrix

• For Neighborhood based CF, we used pre built set of algorithms in Apache Mahout Machine Learning Framework

OUTLINE


HyRES Architecture

• HyRES was organized into the following five modules;1. PWM Storage Module – Persistent store for all data in

HyRES2. Participant Dialog Module (PDM) – Gathers participant

details and exerciese preferences3. Domain Knowledge Module (DKM) – Exercise Science

Domain Knowledge coded in JBoss Drools Expert System4. Options Exploration Module (EM) – Recommendation

funcitonality supported by Apache Mahout Machine Learner

5. System Integration Module (IM) – Coordinates the event, data and control flow across above three modules and manages the Storage

<Interact>

Participant Dialog Module (PDM)

Participant Detail

Domain Knowledge Module (DKM)

DKM Prescriptions

Integration Module (IM)

Rule Execution

Expert Rules

Exercise Selection

Participant

PWM Storage

<Profile>

<Exe

rcis

es>

Exercise Recommendation

EM Recommendations

<Prefer>

Exploration Module (EM)

Collaborative Filtering

Rules.DRL

<Prefer>

<Exe

rcis

es>

<Participant Profile>

<Recommendations>

Authentication and Interaction

HyRES Backend Library Architecture

Funcitonality of HyRES Components• Authentication – Authenticates participants and creates

sessions• Interaction – Coordinates with participant to build the profile• Modification – Changes participant’s profile through the

participant dialog• Storage – Persistant sotrage of participant records• Rule Execution – Expert system management in DKM• Exercise Selection – Combines Expert System output with the

exercise data in the PWM Storage to come up with exercise prescriptions

• Exercise Recommendation – Handles the recommender system to explore more exercise options for the participant

Integration Module (IM)

Authentication and Interaction

Participant Profile

Exploration Module (EM)

Domain Knowledge

Module (DKM)

Exercise Selection

PWM Storage

Participant Dialog Module (PDM)

Stores Participant Data

Obtains Participants Preferences

Builds Participant Profile

Evaluates Participant Profile

Obtains Updated Profile

Obtains ExercisesGenerates Exercise Prescriptions

Processes User Inputs

Trig

gers

Exp

ert S

yste

m

Generates Exercise Recommendations

Coordinates Participant Dialog

HyRES Component Interaction

OUTLINE


PWM Storage• Every participant is associated with four data sets;

1. Profile – Data gathered from participant dialog2. Exercise Prescriptions – Expert System evaluation3. Exercise Preference – Participant’s Responses for prescribed exercise4. Exercise Recommendation – More exercises explored based on

exercise preferences• PWM Storage supports the above four data models based on

three update rates;1. Meta Data – These data define the parameters that are used to

specify exercises and participant profiles2. Static Data – These data do not change after they are entered into

the PWM Store3. Dynamic Data – These data change frequently, perhaps, with each

new interaction with the participants

Participant Profile (PDM & IM)

Exercise Prescriptions (DKM)

Exercise Preferences (PDM & IM)

Exercise Recommendations

(EM)

Generated through Participant Dialog Module

(PDM)

Generated by the Domain Knowledge Module (DKM)

Generated with participant interaction on DKM decisions

Generated by observing the participant preference on

DKM decisions

Participant

Evolution of Participant Profile in HyRES

Meta Data

exercise types exercise levels exercise goals

participant occupations

Static Data

exercises

participants

participant exercise health

Dynamic Data

participant exercise preferences

participant general health

administrators

participant activity history

participant exercise schedules

exercise categories

Multitire Structure of PWM Storage

activity levels

Participant Dialog Module (PDM)• The objective for the Participant Dialog Module is to;– Gather participant information– Present potential recommendations– Collect participant preferences

• To provide participant satisfaction; PDM supports both Mobile and Web interfaces through a RESTful service

RESTful Web Service

HyRES Backend Library

JSON Interface for Participant Dialog Module (PDM)

Internet <HTTP>

PWM Storage

Mobile AppsWeb Apps

PDM IM DKM EM

HyRES Interfaces

LoginRegister

Rejected

Enter health data

Enter information on exercises performed during past 7 days

Enter information on the fitness status

Select exercise categories

Express preferences on prescribed exercises

Get recommendations based on preferences

Get exercise schedule

Rejected

<Existing Participant><New Participant>

Participant’s Activity Flow

Domain Knowledge Module (DKM)• This module encapsulates the theories from the

domain of exercise science as rules• JBoss Drools platform supports seamless and robust

interactions between the Java based implementation of HyRES and Domain Knowledge Module

• The JBoss Drools platform offers a convenient interface via Java objects

• The main object that is exchanged between the DKM and HyRES is the Participant Profile

Pattern Matcher(ReteOO)

Drools Rule Engine

Working Memory

Participant Profile

Domain Knowledge

(Rules)

Modify

Drools Expert System

Participant Attributes Used by DKM Updated by DKM

Gender ✔

Weight (lb) ✔

Height (in) ✔

Age (years) ✔

Blood Pressure ✔

Resting Heart Rate ✔

Daily Calorie Intake ✔

Exercise Goal ✔

Fitness Status ✔

Current Exercise Status ✔ ✔

Basal Metabolic Rate ✔ ✔

Body Mass Index ✔ ✔

Exercise Intensity ✔

Exercise Frequency ✔

Exercise Duration ✔

Participant Object in DKM

Drools Rule Structure• A Rule is made up of two section– ‘When’ Statement : Condition of the Rule– ‘Then’ Statement : Action based on the condition

• Within ‘Then’ statment; it can alter the working memory through modify, retract, update and insert operation on Java objects in the working memory

• JBoss Drools platform allowes us to set rule priorities through the ‘Salience’ attribute

Sample Exercise Rule in Drools

Salience Rule Purpose of Rule

50 Startup Rules Call the rule functions and set the intermediate parameters (BMR, BMI, etc) in the participants profile

40 IPAQ Exercise Rules

Set the initial Exercise Intensity based on the Exercise Ability assigned out of the three exercise levels (Minimal, Moderate and Health Enhancing)

30 Gender Rule Alter the Exercise Intensity for Male and Female participants respectively

30 Age Group Rules

Alter the Exercise Intensity for different age groups

20 Fitness Status Rules

Override the exercise intensity if the participant is subjected to some special constraints (disabled, chronic diseases, bedridden)

10 Exercise Goal Rules

Set the Exercise Frequency and Exercise Duration based on the participant’s goal of doing exercises

0 BMR Calorie Rules

Check whether the expressed goal is realistic along with the participant’s dietary pattern

0 BMI Rules Do any modification to the generated exercise schedule based on the obesity of the participant

Rules and Priorities in DKM

Rule Function Function Purpose

getExerciseLevel Calculate the Exercise Ability of the participant using the answers given for International Physical Activity Questionnaire (IPAQ)

lifeStyleToActivityLevel Determine the day-to-day activity level of the participant based on his/her life style. Participant’s occupation is considered as a key indication of the life style.

getMBICalculation Get the Body Mass Index (BMI) of the participant based on the anthropometric data in the profile

getBMRCalorieCalculation Calculate the Basal Metabolic Rate (BMR) calorie requirement for the participant using the Harris Benedict equation

Rule Functions

Exploration Module (EM)• The Exploration Module (EM) in HyRES has been

designed and implemented on the Recommender System techniques

• We investigated two different CF techniques to design the EM; – Matrix Factorization– Neighborhood Methods

• Participant-Exercise preference matrix is the basis for those CF techniques

• Neighborhood based CF is the currently used implementation in HyRES and it was based on the Apache Mahout ML Framework

Participant-Exercise Preference Matrix (M)Exercises

Parti

cipa

nts

Matrix Factorization (Latent Factor Model)

Stochastic Gradient Descent (SGD) Method

Error: The difference between actual preference and calculated value

Adjustment to Factor Matrices to minimize the RMSE

Neighborhood CF Methods

Pearson Similarity between two participants

Prediction based on a neighborhood

Apache Mahout Library Components

Recommender Data Model

Similarity

Neighborhood

PWM Storage

HyRES

HyRES intercation with Apache Mahout Components

OUTLINE


Examples and Evaluation• We used a set of synthetic participants for evaluation– A synthetic participant is a hypothetical participant that is a

typical representative of an actual participant• The attributes of the synthetic participants were

selected to test the different values allowed in the software system

• Decisions made by Expert System and Recommender System can be compared against the condition and needs of each participant

Age Group Gender Fitness Status

Exercise Goal Exercise Ability

Calorie Intake

BMI

Young (15 < age < 40)

Male Healthy Stay Healthy Minimal > BMR + 500

> 26

Retired (61 < age < 90)

Female Disabled Loose Weight Moderate < BMR – 500

< 18

Mid Age (41 < age < 60)

Chronic Condition

Maintain after Weight Loss

Health Enhancing

Balanced Healthy

Bedridden Prevent Weight Gain

Attributes of Synthetic Participants

User ID Age Gender Weight (lb)Height (inch)

Daily Calorie Intake Fitness Status Occupation Exercise Goal

Vigorous Exercise Minutes

Moderate Exercise Minutes

Light Exercise Minutes

u0 28 Male 180 71 2200 Healthy Desk WorkerPrevent Weight Gain 10 60 280

u1 22 Female 150 64 2000 Healthy Household Loose Weight 20 120 350u2 36 Female 200 64 2800 Chronic Patient Household Stay Healthy 0 20 140u3 46 Male 180 73 1800 Healthy Farmer Stay Healthy 120 350 700

u4 16 Male 190 74 2500 Healthy AthletePrevent Weight Gain 90 160 240

u5 26 Female 160 65 2000 Healthy Health CareMaintain after Weight Loss 0 160 420

u6 31 Male 200 72 2700 Healthy Academics Loose Weight 10 90 240

u7 56 Female 180 62 2100 Disabled Desk WorkerPrevent Weight Gain 0 0 180

u8 76 Male 170 73 2000 Bedridden InactivePrevent Weight Gain 0 0 20

u9 30 Female 190 70 2500 Healthy SoldierMaintain after Weight Loss 60 200 700

u10 40 Male 175 65 2700 Chronic Patient Porter Loose Weight 20 120 350u11 26 Male 154 72 2900 Healthy Student Stay Healthy 140 210 420u12 46 Female 165 66 2000 Bedridden Household Stay Healthy 0 30 75

u13 31 Male 176 60 3000 Healthy LaborerPrevent Weight Gain 50 100 420

u14 36 Female 121 60 2000 Healthy Field worker Stay Healthy 20 300 350u15 38 Female 154 68 2500 Patient Inactive Stay Healthy 0 0 45u16 25 Male 132 63 3000 Healthy Academics Stay Healthy 105 210 420

u17 71 Female 127 72 3200 Chronic Patient HouseholdPrevent Weight Gain 0 25 280

u18 26 Female 110 66 2800 Healthy Student Stay Healthy 210 280 420u19 22 Male 143 70 2600 Healthy Student Stay Healthy 45 210 300u20 41 Male 127 72 2000 Disabled Inactive Stay Healthy 0 0 210u21 26 Male 138 68 3000 Healthy Desk Worker Stay Healthy 300 280 420u22 31 Female 132 64 2200 Healthy Field worker Stay Healthy 0 50 560u23 58 Female 119 60 2300 Chronic Patient Household Stay Healthy 45 80 200u24 26 Male 154 68 2800 Healthy Student Stay Healthy 35 420 420

u25 51 Male 187 72 3000 Disabled CleanerPrevent Weight Gain 20 0 350

Synthetic Participants

u0 u2 u4 u6 u8u10

u12u14

u16u18

u20u22

u240

2

4

6

8

10

12Expert System RS (Exercise based Similarity) RS (Participant based Similarity)

Participants

Max

Inte

nsity

(MET

) Max Intensities Suggested to Synthetic Participants

u0 u2 u4 u6 u8u10

u12u14

u16u18

u20u22

u240

50

100

150

200

250

300

Minutes per Week

Participant

Exer

cise

Min

utes

per

Wee

k

Exercise Durations Suggested to Synthetic Participants

1 2 3 4 50

2

4

6

8

10

12

Desired Exercise Intensity

IPAQ Exercise Level

Reco

mm

ende

d Ex

erci

se In

tens

ity (M

ET)

Minimal Moderate Health Enhancing

u8, u12

u2, u7, u15 u10

u17, u20, u23, u25

u14 u22

u0, u1, u4, u13, u16, u19, u24

u5, u6

u18

u3, u9

u11, u21

Exercise Ability vs. Desired Intensity

1 2 3 4 50

50

100

150

200

250

300Exercise Duration

IPAQ Exercise Level

Exer

cise

Min

utes

per

Wee

k

Minimal Moderate Health Enhancing

u8, u12

u15u2u7

u20u14, u22, u24u4, u16, u23u17, u19u0, u25

u13u5

u10u1

u6

u9

u18, u21u11u3

Exercise Ability vs. Exercise Duration

OUTLINE


Conclusion• We presented a Hybrid Recommender System (HyRES) that can

effectively recommend exercises to participants. After presenting the overall architecture, the detailed design of the major modules were also presented.

• The Participant Dialog Module (PDM) gathered information from the participants based on the guidelines provided in the well-known IPAQ standard.

• The PWM Storage was designed to store participant records in a normalized object oriented manner.

• The Domain Knowledge Module (DKM) represented knowledge from the exercise science domain in rules. Based on the inputs gathered from the participants, the DKM rules determined the duration and intensity of exercises that could be recommended.

Conclusion cont.• The Exploration Module (EM) was designed using a

Collaborative Filtering techniques. Using the output of the DKM as a basis and participant preferences as input, the EM identified additional recommendations that could be recommended to the participants.

• Results based on synthetic participants demonstrated that DKM rules determined the recommended intensities and durations based on several factors and EM recommendations used to align with the decisions made by DKM.

Future Works• In the future, the rule-set in the DKM must be

expanded to incorporate additional factors and a rigorous field trial must be conducted to validate the efficacy of the approach after obtaining appropriate Institutional Review Board (IRB) clearance.

• For the large scale deployment, the Exploration Module should be distributed to enhance the performance; utilizing the support provided through Mahout Scalable ML Framework.

OUTLINE


Demonstration• We have developed a sample client application to

demonstrate the HyRES functionality;– Collects data through user inputs (PDM)– Triggers Expert System to get initial recommendations

(DKM)– Call Recommender Systems to explore more

recommendations (EM)– Presents the summarized exercise schedule to the

participants (IM)– Store participants records in the persistent store (PWM

Storage)• Android App developed to work with the RESTful web

service is also available

Thank You

Hybrid Recommender System Architecture for Personalized Wellness Management

Software

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