A personalized adaptive algorithm for sleep quality prediction using physiological and environmental sensing data

A Personalized Adaptive Algorithm for Sleep Quality Prediction Using Physiological and Environmental Sensing Data A Personalized Adaptive Algorithm for Sleep Quality Prediction using Physiological and[.]

A Personalized Adaptive Algorithm for Sleep Quality Prediction using Physiological and

Environmental Sensing Data

1st Nguyen Thi Phuoc Van

Big data Integration Research Center

NICT Tokyo, Japan

phuocvan@nict.go.jp

2nd Dao Minh Son Big data Integration Research Center

NICT Tokyo, Japan dao@nict.go.jp

3rd Koji Zettsu Big data Integration Research Center

NICT Tokyo, Japan zettsu@nict.go.jp

Abstract—The lacking data from wearable sensors to solve

different problems in the healthcare area is obvious since it is not

easy to find enough volunteers to collect data Moreover, human

reacts very differently to medical treatment/ exercise levels/ stress

and so on Therefore, we need an advanced prediction model

which can reuse the public data and can be adapt to personal

data to predict health parameters This paper introduces a

solution for this issue We present a novel personalized

adap-tive algorithm based on ensemble learning to predict sleeping

efficiency, the proposed framework can be extended to solve

many problems in healthcare applications In this work, the

global model is built based on ensemble learning with common

features from all clients The global model is then combined

with the model from the client with more personalized features

The client model will learn and be updated model every day

Our proposed framework was tested in two data sets PMData

and another private data set and showed better results than

the conventional method The proposed algorithm/ framework is

a great step to solve the prediction problem in healthcare since

each person has their own characteristics, responds differently to

treatments/environment/stressful levels The proposed algorithm

is a big enhancement in building a health navigator system to

enhance human health

Index Terms—Sleeping efficiency; transfer learning; Random

forest; health navigator; activities; adaptive prediction model

I INTRODUCTION

SLEEPING quality has an important role in human life

Poor sleep may lead to many problems like reduction of

concentration and productivity, affecting negatively on blood

sugar and increasing type 2 diabetes risk, reducing the immune

system, or decreasing the performance of social interactions

[1]–[13] Since maintaining good sleeping is a crucial factor

to be perfectly healthy We need to know which factors cause

good sleep, is that possible to predict sleeping quality/ sleep

efficiency from people activities, environmental factors, and

so on The development of wearable sensors, environmental

sensors, embedded systems, and big data makes it possible

to collect information to predict sleeping quality and find the reasons to enhance sleep quality

The previous prediction models were built based on machine learning techniques and introduced quite good results How-ever, each person reacts differently to the physical activities, environmental parameters, and so on Multiple factors affect sleep quality like burned calories, active levels, stress levels Therefore, necessary to establish a personalized adaptive sleep prediction model for each individual is essential to develop the e-healthcare application Personalized/adaptive models have been utilized in other areas [14]–[16] For the data center resource utilization estimation [15], the adaptive prediction model refers to the selection of the best model in the given time window Another recent paper [16] proposed a model which is adjusted seasonality and removed the error cycle Aside from personalized adaptive for each user, the model could be more reliable if it gets knowledge from a large mass database - this idea leads to the using of transfer learning in sleep science [17], [18] Phan et al [18] used transfer learning for sleep staging and showed the impressive improvement of

a prediction model The idea of using transfer learning is a good foundation to build the adaptive model in sleep quality prediction

Even though the personalized adaptive models are applied in many fields but not much in sleep research Transfer learning was also used to improve sleep stages classification Besides sleep stages, sleep efficiency is one of the substantial sleep attributes sleep efficiency is the proportion between sleep duration and total time spent on the bed Currently, we do not have personalized adaptive models to predict sleep efficiency [19] For this reason, this work investigates a personalized

adaptive algorithm for sleep efficiency from activities,

envi-ronmental factors and compares it to a recent popular model

Our proposed prediction model gives better results than the

transfer deep learning models and Bayesian Replicator Neural

Network on the public and data sets Our proposed framework

fosters sleep quality prediction in the e-healthcare system

This method is reusable in different applications by using a

trained model on the public data on private data event private

data has more features to improve the prediction accuracy

The contribution of this study is as follows:

• We introduce a program/algorithm to pre-process public

data for sleep quality prediction problems This program

created a data frame that contains features and sleep

attributes The data frame can be used to find

correla-tion between activities and sleep attributes and to build

prediction model for sleep efficiency

• The personalized adaptive model to predict sleep

effi-ciency was developed and shows the great capability

in reusing public data to solve the accuracy modeling

problem in the private data Our framework is utilized to

solve the lacking data issue in building the model in the

healthcare area

II RELATED WORK

A Association between sleep quality and environmental

fac-tors

Several studies focus on the finding association rules

be-tween environment and sleep duration [20]–[23] They

consid-ered the relationship between the natural/ social environment

and sleep quality In these kinds of studies, the sleep quality

is evaluated by questionnaires- Pittsburgh Sleep Quality Index

(PSQI) Reference [20] found the relation between

environ-ment and sleep quality in the summer of Japan This research

showed that the air-condition and light conditions might affect

the sleep of volunteers Controlling the room temperature and

light can improve sleep quality More broadly considered, the

study [21] considered the relationship between air pollution

and sleep duration in young people in Beijing China The sleep

duration was collected by doing the sleep survey- Chinese

version P SQI metric The data of sleep and environment were

collected in the five-year duration (2013 − 2018) from 16, 889

students This work concluded that sleep duration reduction

has a high association with air pollution Another study [22]

found the sleep disorder in old people and the environment

This research utilized the health database of Chinese elderly

in Ningbo province in the duration from 2008 to 2017 The

data information of visiting the hospital of old people at the

age (60+) was collected along with the daily air pollution

parameters like nitrogen dioxide, sulfur dioxide, inhale-able

particles, and so forth This work discovered that the air pollution exposure in the old people had a high association with the frequency of doctor/clinic visits to solve the sleep disorder problems

To find association rules between sleep quality/duration and environmental factors, in almost all studies, the questionnaire

- P SQI was used as a sleep evaluation metric However, this kind of evaluation is not always accurate since answers are subjective Moreover, sometimes, people forget to answer the question at the right time and even forget the feeling of them-self after sleep

B Association between sleep quality and daily activities The development of wearable sensors makes it easy for us

to collect stages of sleeping and obtain more accurate sleeping quality metrics Moreover, physical activities also can be col-lected by wearable sensors [24], [25] Aarti Sathyanarayana et

al [2] develop a deep learning-based model to predict sleep-ing quality (good/poor) from tracksleep-ing physical activities in the awake time Their prediction model showed a good result (up to 94 %), much higher than the logistic regression model The physiological signals are also used to predict sleeping quality in the caregivers of people with dementia (CP W D) group [26] The wearable sensor (E4 Empatica wristband) was used to collect body movement (through accelerometer), heart rate, electrodermal activity, and skin temperature Those features are used to measure sleeping quality and restfulness

in CP W D and yielded a good prediction result - up to 75% III PERSONALIZEDADAPTIVE ALGORITHM The objectives of this method are (1) to choose the best global model which is generated from common features of public data, and (2) to combine the global model and personal model with extra private features to build the personalized adaptive model for each individual The random forest re-gression based on the least-squares error criterion is used in the global model for its flexibility, easy comprehensibility, and computational efficiency The tree-based regression model provides the propositional logic representation of predictor space in a tree form A logical test on a predictor variable

is done in each internal node of a tree

The framework is started with the constructed with the global model This model is then distributed to each client to estab-lish the client model The proposed framework is illustrated

in Fig 1 In this work, the global model is built from global data In the global data, only common features of all clients are used to train the model Global data is divided into two parts, 75% for training and 25% for testing We utilized the stacking ensemble learning in the global model to get better performance of the model After checking the model on the

testing data, if the model has good performance it will be sent

to each client to reuse The algorithm of the global model is

described in III-A

At the client, the global is combined to the other model which

is shaped from client features The details of the algorithm at

each client is discussed in the III-B

Fig 1 Proposed personalized adaptive framework

A Proposed global model

The global model is based on ensemble learning to combine

two models into one to optimize the prediction results Model

1 and model 2 are Random Forest Regression models The

optimized tree of these models was calculated by minimizing

the Mean Square Error (M SE)

1

ns

ns

X

i

(yi− ypre(xi))2 (1)

where ns is the number of data samples; yi is output value i,

ypre(xi) is the predicted value of the output based on the set

of feature x order i

An example of a brand of tree based model is described in

Fig 2 An example of brand of a random forest regression

2 In each node k the error (M SE) is calculated as follows [27]:

M SEk = 1

nk

k

X

1

yi− 1

nk

k

X

1

yi

!2

(2)

where nk is the number samples of node k The error of a tree is defined as the average of errors in its leaves The tree is developed based on two sets of splitting rules, the goal of building the tree/splitting rule is to minimize the error of this tree This rule is used to build model 1 and model 2 - two main components to build the global model The algorithm of the global model is presented as follows:

B Personalized Adaptive client model This model is built from global model and personal model based on the personal data The detail of algorithm is pre-sented in Algorithm 2 The first step is to load the global model, after that, based on number of important features in the client we choose the structure of local model This model

is then combined with global model by stacking technique to make personalized Adaptive model (M odelCA)

IV DATA SETS

A PMData dataset PMData data set (https : //osf.io/vx4bk/wiki/home/) was collected by the Fitbit Versa 2 smartwatch, PMSys sports logging, and google form for the duration of 150 days of

16 participants In total, we have 1663 days of data Data from Fitbit consists of information about burned calories/ min, moved distance/min, heartbeats/min, steps/min, time in different heartbeat zone, light/moderate/very active minutes per day, sleep score, report about meals, report about wellness (fatigue, mood, readiness ) Most of files are formatted as

*.json and *.csv This data was used for competition and showed the possibility for analysis In this work, the activities information like distances, sedentary, steps, sleeping is used

to validate our proposed sleeping efficiency prediction model The process diagram to collect and gather PMData is showed

in the Fig 3 and the algorithm is shown in 3

We can see in Fig 3 that the extra features including heartbeat (average heartbeat of the person in a day), pm sise (the size

of particulars in the air), humidity, pm10 (particulate matter lower than 10µm), temperature, CO2(level of carbon dioxide) are considered to build the adaptive model for each person

B Fitness data set This data consists of 9 sub-set data Each sub-set data has information about the activities and environment of a volunteer in the duration of 90 days The ethics of the data collection campaign was approved by The National Institute

Algorithm 1: Proposed global model

Input: Input: Data consists of all common features

Output: Output: Global model

Initialization;

1: Select group1 of features

2: Select group2 of features

3: Build model 1

Input1: set of training data (ntrain data points)

group1 with c features,

{hxtrain i, ytrainii} , i = 1, 2, 3 ntrain; xtraini =

{f eature0, f eature1, , f eaturec}

Output1: a regression prediction model 1

Set the initial tree structure

T∗=< initialsplit >

For all possible tree Tv

Calculate M SE(Tv)

Find the Tbest where M SE(Tbest) =

minimum value of M SE(Tv)

end For

If M SE(Tbest) > M SE(T∗)

Create model1 with T∗

else

Create model1 with Tbest

4: Build model 2

Input2: set of training data (ntrain data points)

group1 with d features (d > c),

{hxtraind i, ytrainii} , i = 1, 2, 3 ntrain; xtraindi =

{f eaturec+1, f eaturec+2, , f eatured}

Output2: a regression prediction model 2

Set the initial tree structure

T d∗=< initialsplit >

For all possible tree Tv

Calculate M SE(T dv)

Find the T dbest where M SE(T dbest) =

minimum value of M SE(T dv)

end For

If M SE(T dbest) > M SE(T d∗)

Create model2 with T d∗

else

Create model2 with T dbest

5: Combine model1 and model 2 to a global model

6: Check performance of global on testing data

7: Save global model

Return: Output: Global model

Algorithm 2: Proposed client model Input: Global model, Client data consists of all common features and personal features Output: Client prediction model

Initialization;

1: Load global model 2: Choose the structure of client model 3: Set the initial training data set Xtrain, ytrain

4: Build the client model in the similar way of model1

in the global algorithm

5: Combine global model and client model to make the personalized Adaptive model (M odelCA) 6: Predict the next outcome based on previous data For i in testing data

ypredictedi+1 = apply (M odelCA) on data set

Xtrain∪ Xi

Error(i + 1) = ypredictedi+1− ylabeli+1

end For 7: Evaluate the performance of adaptive model Calculate Root Mean Square Error Calculate Mean Absolute Error Return: Output: Client model

Fig 3 Phases to collect and preprocess PMData

of Information and Communications Technology, Japan Each person wears a smartwatch Fitbit sense to collect activities and sleeping data Volunteers were provided smartphones to answer the questionnaire and the environmental sensors were installed in their bedroom to collect surrounding data (noise,

P M 25, temperature, CO2, P M size, humidity) In total, 783 days of data were collected The data from Fitbit sense was

Algorithm 3: Pmdata- Data prepossessing steps

Require: Input: RaW data from Fitbit, PMSys, app,

and google form

Ensure: Output: processed Data

Initialization;

1: Define the function dateonly(time)

Calculate the date of date and time string

return date string

2: Define the function loadjs(∗.json)

load *.json file

return content of json file in dictionary format

3: Define the function loadalljsonfit(path)

Use loadjs function to load data (∗.json files)

in the Fitbit folder then convert data into data frame

4: Load and change sleep.json to dataframe by loadjs

function and pd.DataFrame

5: Load data from exercise.json by loadjs function

6: Load data from *.json file

Calculate feature values for each day

Create a dataframe has information of date and

feature values

7: Load data from *.csv file

8: Merge all above data to a data frame named

based on the common date,

the data of missing days will be removed

9: return alldata

10: end

processed in a similar of processing PMData This data is used

to validate the personalized adaptive model again to show the

performance of our model

V RESULTS AND DISCUSSION

In this section, the results of the proposed model are taken

into account and compared with the conventional one For

both data set, we applied Leave One Out (LOO) technique to

check the performance of the proposed model

A Results on PMData data set

After cleaning and processing PMData, data of 15

par-ticipants was used to build and validate the model.150-day

activities data of each person is considered as a subset Data

of 14 people with 6 common features (very active minutes,

moderate active minutes, light active minutes, sedentary

min-utes, distance, steps ) was used to form a global model

to predict sleeping efficiency (ranged 61-100, mean value

= 87.8, standard deviation = 3.7) The rest data (1 person)

with 10 features (6 common features in the global model and

four extra features are time in four zones of heartbeat) was

used to validate the proposed framework

Several state-of-art models mentioned in [18], [28] also ap-plied, in the same manner, to compare with the proposed one Two deep transfer learning models with two hidden layers with shape(30, 3, 1) and shape(30, 4, 1) are reviewed to compare with the proposed model

In addition, we also make trial run on the Bayesian Replicator Neural Network [28] and Tree-based models which are pop-ular for small data sets A poppop-ular accuracy metrics - Root Mean Square Error (RM SE) is used to compare the error prediction between models Figure 4 shows that in all cases

Fig 4 Compare the RMSE of proposed model and recent models on PMData the proposed framework has higher prediction accuracy/less error The average RM SE of our framework is 3.5 while the

RM SE of other prediction methods are larger than 4 For example, at users ID 10 the different error between the Tree-based transfer learning and personalized adaptive model is 1.7 ( around 40%)

To check the practical application of our proposed framework,

we check its performance on another data set which was collected in different locations and participants did various types of exercises In the next section, the RM SE of some state-of-art models and proposed model on the fitness data are presented and compared

B Results on group fitness data Steps to reuse public data to build the personalized adaptive prediction model for personal sleeping efficiency prediction are demonstrated in Figure 5 The PMData is used to create the global model 6 activity features in PMData are reused to construct the global model, 4 more environmental parameters

in the client are added to create the personalized adaptive model

Figure 5 The performance of the proposed frame work is ex-amined through RM SE Figure 6 reveals again the superiority

of the adaptive model Our proposed model has lowest error The average error on our model decrease 25% and 50%in comparison with deep transfer learning and Bayesian RNN

Fig 5 Steps to apply personalized adaptive framework on fitness group data

Fig 6 Compare the RMSE of proposed model and state-of-art models on

group fitness data

model, respectively Moreover, this error in this private data

yields the better result than in public data since in private

data we obtained more feature from environment, those

fea-tures (like noise, temperature) have high impact on sleeping

efficiency of people

C Discussion

It is obvious that the healthcare prediction model prob-lem always faces the lacking of data since the number of volunteers is not large enough or missing the diversity of participants in gender, age range, health condition, and so

on In addition, each person responds very differently to the level of exercise practicing, weather, air pollution, medical treatment, and so on Our personalized adaptive prediction for sleeping efficiency demonstrates the first important point that the prediction model in healthcare applications should be built on the personal level with data over a long period The second good point in our framework is to reuse the public data to increase the reliability and accuracy of the personal model This point is proofed in the observation of the RM SE values of public data and private data, the private data shows less error

VI CONCLUSION Our proposed algorithm shows a high accuracy prediction

in comparison with the traditional one We use the sleeping efficiency prediction from activities and the environment to demonstrate the advantages of our proposed model Our framework opens a good approach to solve the modeling issue

in the healthcare area This framework confirms the possibility

of using public data (large data set) for private applications with small data set but compatible features Moreover, this framework hints at the use of federated learning that all clients contribute common features to build the global model Each client then can use a global model to create its model

ACKNOWLEDGMENT

We would like to thank GreenBlue1and TAOS2for sharing materials We also would like to thank for the contribution of DLC to help us to collect data for this research

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