Implementation of IoT, Wearable Devices, Google Assistant and

Google Cloud Platform for Elderly Home Care System

Jung-Tang Huang

, Li-Ying Chang

and Hsin-Chang Lin

Department of Institute of Mechatronic Engineering, National Taipei University of Technology, Taipei, Taiwan

Department of Institute of Mechanical and Electrical Engineering, National Taipei University of Technology, Taipei,

Taiwan

Keywords: IoT, Cloud, GCP, Wearable Devices, Data Analysis.

Abstract: The purpose of this research is dedicated to designing the care system. By using Google assistant speaker,

various sensors, web page, and cloud data processing to design an Internet of Things environment combining

health information and various parameters to improve the quality of the care system. We use wearable devices

to transmit physiological information, then collect data through Bluetooth sensors and upload them to the

database via edge devices. At the same time, it monitors unusual values at any time. Then, it notifies users

through google assistant to trigger Google Home System. We carry out cloud data analysis and optimize

dialogue patterns by obtaining physiological information, escorting services, recording conversations, and

other forms of active questioning. Through using conversation feedbacks as data, we can also generate simple

data analysis, fill out various questionnaires by using the web pages. With this complete care system, the

cloud data is integrated and networked to provide a better care system for the elderly.

INTRODUCTION

The Internet of Things means that there is a large

number of composite data through statistics and

analysis, which can get relevant information that was

previously ignored by a single data. Conducting to

solving problems in various fields such as smart home

care, this research focuses on providing services

between smart homes and elderly care, by using the

data collected from the sensor and Google Home

System to perform a series of verification, processing,

classification, storage, statistics, and visualization on

the data for the user or their caregiver. Those data can

help them to make plans for improving their health.

The main purpose of the Internet of Things

application is to provide humanized services, making

family life more comfortable, safe and energy-

efficient, and processing some of the more

burdensome computing by the cloud, which can

quickly adapt to changing loads. Therefore, the

integration of smart homes into the cloud and

obtaining more information from the cloud will help

provide more humanized services. This is the goal of

https://orcid.org/0000-0001-5655-2048

https://orcid.org/0000-0002-6457-712X

smart homes for IoT applications now (Haijun Gu,

Yufeng Diao, Wei Liu, and Xueqian Zhang, 2011).

The services provided by the smart home can be

roughly divided into many types (Xiaojing Ye and

Junwei Huang, 2011). (1) Environment: air-

conditioning, water, lighting. (2) Safety: fall, injury,

and break in. (3) Entertainment: TV, stereo. (4)

Electrical appliances: Recipe suggestions, automatic

cooking, cleaning, refrigerator inventory. (5)

Message and communication: alarm, home calendar,

remote control. (6) Health: behavior, medication,

sleep, etc. In response to demand, well-known

companies such as Google, Amazon, Microsoft, IBM,

etc. have all proposed SaaS (Software as a Service),

PaaS (Platform as a Service), and IaaS (Infrastructure

as a Service). This research is to develop PaaS among

them, establish a service-oriented cloud computing

architecture and provide users with SaaS. Users can

use our existing services through simple settings. In

reference (M. M. E. Mahmoud et al., 2018), there is a

new term named COT (Cloud of Things), after

integrating the cloud and IoT, all IoT devices can be

accessed through the cloud as a service, the role of the

cloud in COT is a middleware between things, this

Huang, J., Chang, L. and Lin, H.

Implementation of IoT, Wearable Devices, Google Assistant and Google Cloud Platform for Elderly Home Care System.

DOI: 10.5220/0010473102030212

In Proceedings of the 7th International Conference on Information and Communication Technologies for Ageing Well and e-Health (ICT4AWE 2021), pages 203-212

ISBN: 978-989-758-506-7

203

research on health services is still focused on individual

sensor values. As the result, it cannot clearly

understand the impact of external factors on users.

In this study, with Bluetooth devices as the main

body, we integrate indoor positioning services, and

various interactive devices as the collocation mesh

network of Bluetooth nodes which are used to upload

data to the cloud. In order to help elderly easier to use

the system, we use Google Home Nest Mini to notify

abnormal physiological signal from the wearable

devices and design dialog system to give them

advices about health. At the same time, we record

these conversations to help doctors or their

caregivers. On the other hand, doctor or their

caregiver can monitor, remotely control and obtain

statistical behavior information by web in real time.

Unlike reference (D. C. Yacchirema et al., 2018),

with a long-term behavioral record, it is possible to

understand what bad habits may cause problems

before getting sick and be proactively alerted.

Medical information and long-term behavioral

records help us to find out what the real causes are.

IOT SYSTEM ARCHITECTURE

2.1 System Architecture Level

The system combined light source devices and

Bluetooth devices. It uses wearable devices and fixed

sensors for positioning tracking, activity monitoring,

behavioral mode judgment, and fall notifications. By

building a network with each other through Bluetooth

wireless communication technology, sensors can

automatically sense and count data. The Bluetooth

mesh network pushes the data to the terminal and

uploads data to the cloud by Wi-Fi and stores data in

the cloud database. This system has 3 ways to notify

or show data for users, web interface, asking for

information from Google Nest Mini or notifying

messages by using Raspberry Pi.

The IoT system architecture is mainly divided into

six levels. The architecture diagram is abstractly

presented in “Figure 1”.

2.2 Wearables and Fixed Devices

The sensors of this system are wearable devices, fixed

devices, and Google Home Nest Mini.

Wearable devices are Bluetooth watch and

Bluetooth tag, as shown in “Figure 2”. Bluetooth

watch uploads physiological data, like heart beat rate

per minute. The Bluetooth tag is designed to detect

the user's movement and their location.

Figure 1: System architecture hierarchy diagram.

Fixed sensor means contact sensors and Google

Home Nest Mini. They both are triggered by a person.

The difference is the way how to trigger them. As

shown in “Figure 3”, such as magnetic reed switch,

seat cushions and pedal mats, they are triggered by

physical contact. And Google Home Nest Mini is

triggered by audio messages from user.

Figure 2: Bluetooth watch and Bluetooth tag.

Figure 3: Contact sensor, cushion (left), step cushion

(right).

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Google Home Nest Mini is running based on GCP

services that help us to create dialog system to process

audio messages. In the processing, it can classify

different dialog intents, events, responses and

records. On the other hand, the design of the smart

seat cushions and pedal mats uses silicone rubber as

the dielectric material. Through the calibration

algorithm, the capacitance value is converted into the

corresponding pressure for comparison and analysis

to determine the sitting posture, for example, normal

sitting, forward, left, right. By recording the position

of the center of gravity of the posture and analyze its

proportion, it may correct the elderly's sitting posture

to avoid scoliosis and elderly falling.

2.3 IOT Edge Computing Device

An edge computing device is located on the edge be-

tween two networks. Edge computing is the

decomposition of large services that were completely

processed by the data center through cutting the service

into smaller and more manageable parts, scattered to

the edge nodes for calculation (W. Shi et al., 2016).

Edge computing can speed up data processing and

transmission speed, reduce network latency. Under

such a structure, the analysis of data will be closer to

the source of the data, so it is more suitable for

processing a large amount data collected by sensors.

2.4 Bluetooth Mesh

The Bluetooth mesh network is constructed by many

bNodes and a bwRouter. The Node device is

equipped with two Bluetooth roles central (Master)

and peripheral (Slave). The former will scan the

broadcast channel at 20 ms intervals, and only receive

our proprietary protocol. Master is transmitted the

data of the communication protocol to the slave

station through the UART communication interface.

The latter is in addition to provide broadcast location

packets for wearable device positioning, the data sent

by the host will be considered as priority forwarding

data. The data will be continued to send to the next

bNode until the master scans its own return packet

and notifies the slave device to stop broadcasting.

This design not only ensures that each piece of data

reaches the next bNode, but also reduces end-to-end

latency. Based on this design, it can build a Bluetooth

mesh network. “Figure 4” is describe the bNode

workflow among multiple bNodes.

The bwRouter consists of Bluetooth and

Raspberry Pi. It inherits the bNode function, and also

it is the top layer of the Bluetooth mesh network. It

acts as a communication bridge between BLE and

Figure 4: bNode upload and transfer process

Diagram.

WIFI. BLE uses the UART communication interface

to send data to the Raspberry Pi, and then uploaded to

the cloud via WIFI for data storage and analysis. The

bwRouter can send acknowledgement to the bNode

or send commands to the lower-layer devices of the

BLE mesh network to implement a two-way

transmission system.

2.5 BLE Initial Setting

Mobile phone application plays a role of setting

terminal as shows in “Figure 5”. The system uses

Android and IOS phones as the main mobile devices.

Bluetooth mesh networks use a method called

"Flooding" to publish and relay messages. In order to

let Flooding have direction, the mobile phone

establishes a connection between the bNode,

bwRouter and the fixed devices and write the

information. This information is obtained from the

Google Map in mobile phone application where gives

the absolute position to itself and let each bNode

know the location of the bwRouter, and then writes

the mesh group to each bNode. This method provides

bNode master judging bwRouter distance and makes

the data transmission directional. In addition to

setting the latitude and longitude, it can also divide

the area, such as restaurant, bathroom, living room,

balcony and kitchen.

Figure 5: IOS APP setting interface.

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CLOUD SYSTEM

ARCHITECTURE

This research uses some of the cloud capabilities of

Google Cloud Platform such as Web development

framework, related development tools and Google

Home Nest Mini. In order to store personnel indoor

positioning and behavior data collected by sensors

into the NoSQL database for web and APP request,

batch output historical data, output statistical reports

through data ETL (Extract-Transform-Load), analyze

of elderly behavior, and push messages through social

media and smart speakers (Google Home Nest Mini).

It can analyze elderly behavior, and push messages

through social media and smart speakers (Google

Home Nest Mini). There will be detailed introduction

and description below. The cloud architecture

diagram of this system is shown in “Figure 6”.

Figure 6: Cloud system architecture diagram.

In this system, we use Google Home Nest Mini in

many ways, such as notify user, company with user

by messages and record messages about geriatric

depression scale (Keelung City Government,2017) to

take care of their health. The dialog flow is shown in

“Figure 7”.

At the beginning, users use their voice to awake

Google Home Nest Mini and it will response “How

are you?”. If users keep talking with Google Home

Nest Mini, it will take those messages to trigger

Firebase Function that Firebase Function will save

messages to Firebase and push messages to Google

Home Nest Mini. In “Figure 7”, we take problems in

Geriatric Depression Scale (Keelung City

Government, 2017) for example. When user tell it

their “feeling”, it will trigger Firebase Functions to

collect the questions by “feeling” and respond a

random question in the questions. Then users answer

the question, Firebase functions will save messages,

score each answers and push messages back to

comfort users.

In order to collect the messages, we coded lots of

functions and designed different conversation

templates to create each dialog flow in Google Home

Nest mini.

Figure 7: Dialog process in Google Home.

3.1 Angular Framework

It is one of the UI service webpages in the system. In

order to efficiently make the data collected by a large

number of IOT sensors responded to users in real

time, this research uses Angular to build a client

application platform. Dialogflow

3.2 Dialogflow

Dialogflow is a natural language understanding

platform that makes it easy to design and integrate a

conversational user interface into mobile app, web

application, device, bot, interactive voice response

system, and so on. Using Dialogflow, we can provide

new and engaging ways for users to interact with

Google Home Nest Mini.

Dialogflow can analyze multiple types of input

from user, including text or audio inputs (like from a

phone or voice recording). As the result, we can use

it to design dialog and set fulfiilment webhook with

Firebase Functions that we can program those

parameters, give response to Google Home Nest Mini

and upload data to Firebase Firestore.

3.3 Actions on Google

Actions on Google is the developer platform for

creating conversational apps for the Google Assistant

and publishing them to Google Home, Android, the

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Google Assistant app on iPhone, and beyond.

It is used to deploy our application on Google

Home Nest mini and manage with different GCP

services.

3.4 Google Home App

The Google Home app helps us to set up and control

Google Nest, Google Home, and Chromecast devices.

We can control thousands of compatible lights,

cameras, speakers and more, all from a single app, as

well as check reminders and recent notifications.

Google Home app also help us to get wifi mac

address to set up Raspberry Pi, distinguish different

devices and use voice-match with g-mail to login

user.

3.5 Cloud Functions for Firebase

Because the user interface of this system includes

web pages, Android IOS APP and Google Home Nest

Mini. User often adds, deletes, modifies, and queries

structural fields at the same level of the object

database to unify specifications, development and

maintenance. The operation of the database at the

application layer of this system adopts the same API

specifications.

This service allows developers to deploy back-end

environments, code to cloud services, eliminate the

need to set up, maintain, expand, manage, and

provide services endlessly.

3.6 Cloud Firestore for Firebase

Firestore is a flexible and extensible NoSQL database

that can synchronize data between client applications

through a real-time listener, and provide offline

support for APP and web applications. Whenever the

data that the client application is listening to changes,

the system will notify through a snapshot of the data

and only retrieve the new changes. By employing

Firestore we can modify the database only in response

to new physiological data in the care system, without

tthe need to significantly reset and modify the entire

system back end. At the same time, it has the ability

to trigger events on the database backend. When the

sensor collects new data, the front end web page and

app screen can be actively updated by the back end,

without the front end actively refreshing, significantly

reduce the use of front end performance, and can load

a larger amount of IOT care sensor data for

visualization.

3.7 Cloud Storage for Firebase

The storage space of this system is not only for

accessing media files such as images, audio, video,

etc. Compared with well-known cloud storage spaces

such as Google Drive, cloud storage can be more

flexible in choosing plans such as charging and data

retention time based on usage. And there are multiple

SDKs to easily integrate different systems. At the

same time, the follow-up of this research will

introduce how to extract and transpose the Firestore

object data for analysis later. Cloud storage can be

flexibly integrated with other cloud services without

unnecessary development costs, and it can

synchronize the organized data to cloud storage.

3.8 Cloud Pub/Sub

The publish & subscribe mode is an instant

messaging service that allows information to be

transferred between different applications.

3.9 Cloud Dataflow

Dataflow is a fully-hosted, high-speed, integrated

streaming and batch data processing service that

minimizes latency, processing time, and costs by

automatically scheduling resources and batch

processing functions. In addition, there is no need to

rely on a server when deploying and managing

resources, so this research uses the Dataflow service

to subscribe to the pubsub topic to extract and convert

each history record in the database into a json file and

store it in cloud storage for subsequent data

transposition.

3.10 Cloud Dataprep

Cloud Dataprep is a smart data service that uses a

visual interface to easily explore, clean, and prepare

structured and unstructured data for data analysis,

reporting, and machine learning operations.

In this research, Dataprep was used to

automatically convert the data extracted by dataflow,

format, clean, label, and combine the live data of

different objects collected by fixed devices and

wearable devices so that subsequent research can

focus on analyzing the data.

3.11 BigQuery

BigQuery is a serverless enterprise data warehousing

service from Google that not only has high scalability,

but also excellent cost-effectiveness, which can help

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improve our data analysis work efficiency (O.

Dawelbeit and R. McCrindle, DC, 2016).

BigQuery is based on a custom schema and data

from object storage and spreadsheets, thus it can

create a logical data warehouse, and analyze all batch

and streaming data. In this study, batches of table data

in the custom view model format were transferred

from Dataprep to BigQuery for large-scale IOT

device data streaming storage and analysis.

3.12 Cloud Data Studio

This research uses Google's data visualization tool

Cloud Data Studio, which is a system’s big data

statistics report UI service, to create custom

visualizations by turning data into compelling visual

content and reports. We link Data Studio to a

BigQuery by transposing dataset in advance via

Cloud Dataprep. With BigQuery BI Engine the

system can get analysis in a very short time.

EXPERIMENTAL STEPS AND

DATA

4.1 Experimental Field Environment

The experimental field is a 45 x 31 meters area

located on the 7th floor of the Tzung-He hall, which

is in the National Taipei University of Technology.

The 31 Bluetooth light source devices (bNodes) are

subordinated as shown in “Figure 8”. Google Home

Nest Mini are set in the center of room 709-2 and 709-

3. Experiments were conducted every day for 10 days

from 1:00 to 6:00 pm, and data on personal behavior

patterns were recorded in the database.

Figure 8: Experimental field plan & bNode installation

location.

In this experiment, we obtained a total of 11938

row data of tag-records of mobile devices; a total of

10594 row data of device-records; 200 row data of

people-records; 250962 raw data of Bluetooth watch

records; and test the Google Home Mini by using

13504 unusual data. The statistics are shown in

Table.1. As People-records is an individual's

behavioral statistics for the day, there will be only one

piece of historical data per day.

The design of the experimental data objects and

the meaning they represent will be explained in the

next paragraph.

Table 1: 10-day experimental data quantity.

Data field Numbers of row data

Tag-records 11938

Device-records 10594

People-records 200

Bluetooth watch records 250962

unusual data 13504

4.2 Data Model Design

This system database is mainly based on Firestore.

The data structure design concept is permission

security, optimized query, and reuse. “Figure 9” is the

Firestore database object design diagram which is

presented in the form of a multi-tree. The following

are the object structure nodes definition:



Personal-account: Record user account, phone,

permissions, device password and setup time, user

information covers individuals and organizations.



Locations: Record one or more addresses.



Floors: Inherit the address information of the

previous layer, multiple floor information,

including the latitude and longitude of the floor

boundary and the floor plan of this floor.



Devices: Inherit the information of the previous

floor and records the current status of the fixed

devices on this floor. The fixed device

information includes fire detection devices, seat

cushions, pedal mats and reed switches, etc.



Devices-records: Inherit a fixed device whose

content is the contact history information of this

device.



Mobile-devices: To this end, the user's mobile

phone or tablet records the alias that the user gives

to this tablet or mobile phone and the necessary

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information for the push function.



Mobile-tags: This is a wearable device. The

current wearable device of the system is a

bracelet, a fixed device that records the current

address, floor, movement and contact of the

bracelet.



Tag-records: Record historical information from

the previous wearable device and contains

positioning latitude and longitude information.



Messages: It records the content of the message

from the web, APP or Google Home Nest Mini,

the source informations.



Peoples: As mentioned in the personal-account,

users can be divided into individuals and

organizations. When the user is an individual, this

node records the current user or the user's family,

their current behavior and the wearing device.

Nodes record the behavior, physical data, and

messages of current employees or caregivers from

Google Home Nest Mini and the devices they

wear.



People-records: The inherited parent node records

the historical information of a person's behavior

physical data, and messages.

Figure 9: Firestore data structure design.

4.3 Web Monitoring Status

The web is mainly divided into monitoring equipment

status, personnel status, and equipment status.

Through the webpage, we can instantly monitor the

position of personnel and the status of related

equipment. “Figure 10” presents indoor positioning

information. When an event (fall, fire, break-in, etc.)

occurs, the user's location information and floor

information can let relevant personnel know.

If the wearable device paired with a related

device, after the data is uploaded to the cloud, the

Figure 10: Real-time indoor infographic.

user's information will be integrated to know which

device the user interacts with, environmental

variables are obtained as well, and then keep track of

all the above information. That is the user's time,

position, and behavioral data are continuously

recorded in the database. From “Figure 11”, we can

know that the real-time information of the wearers on

the day, the currently worn device, and the currently

contacted device.

Figure 11: Real-time indoor infographic.

In order to help user, there is also charts and data.

From “Figure 12”, we show every users physical data,

such as blood pressure, temperature and heart beat

rate, in real time.

Figure 12: Real-time physical data.

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And create charts (weekly physical data) by click

the column of user as shown in “Figure 13”.

Figure 13: Weekly physical data charts.

In addition to the condition of the elderly, this

system also cares about the management of a large

number of sensors. Therefore, the device list provides

this user not limited to a single address floor, but the

real-time status of all the devices they own, because

there are too many types and numbers of devices. As

shown in “Figure 14”, this table provides each field

that can be sorted and searched in order, or we can

manually search for the device by entering a keyword.

The fields are in order: name, type, MAC, altitude,

longitude, latitude, status, number of data, setup time,

and latest update time.

Figure 14: Real-time device infographic.

4.4 Google Home System

We use Google Home as notify system and a way to

get points of Elderly Depression Scale to make sure

our user are healthy.

As a notify system, we use codes to get the data

of physical data when the value is too high or low.

And the data will be downloaded, create messages

and check the location of user and the nearest Google

Home Nest Mini. Then it will push the message to the

user.

User can talk to Google Home Nest Mini get their

health condition. At the same time, we will ask them

some question in geriatric depression scale to make

sure their mental health and change the dialog

message.

4.5 Data ETL

(Extract-Transform- Load)

Except real-time monitoring of the Firestore and web,

in order to see the long-term life behavior patterns of

the elderly, we will periodically export People-

records, Tag-records, Device-records data of the

object database through Cloud Dataflow and Cloud

DataPrep to set the data recipe. Then Dataprep's

graphical interface perform data cleaning, merging,

labeling and other data preprocessing actions, as

shown in “Figure 15”. Finally, we output personal

behavior pattern analysis and statistics Table data into

BigQuery database and Cloud Data Studio to link

output visual report as shown in “Figur. 16”.

Because People-records data includes the mac

address of the wearable device worn by the

experimenter on the day. Tag-records data includes

the Tag's mac address, the contacted Device mac

address and positioning coordinates. Device- records

includes the Device's mac address and the contacted

event type. We can combine the above three types of

historical data records through the above-mentioned

ETL data transposition to obtain the experimenter’s

long-term who-when-where-which features and

behavioral pattern information. In addition to instant

notifications of emergency events such as falls and

fires, when the behavior of the elderly is too different

from past behavioral patterns, the system can also

broadcast care reminders, or notify the family to

achieve the effect of smart care.

Figure 15: Dataprep Data processing interface.

The advantage of the design in this research

system is that data can be passed through fixed

DataPrep recipes. Historical data can be ETL

repeatedly stored in BigQuery to accumulate big data.

There is no need to do data preprocessing all the time.

And the Data Studio reports are continuously updated

through BI Engine and SQL. In addition to through

the filter of Data Studio, we can easily search for

individuals or data on specific dates and experimental

time intervals for analysis.

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Figure 16: Data studio behavioral big data statistics report.

Figure 17: Tag-records data detail.

Figure 18: Daily activity pattern of 2 students under test.

According to the statistics of the IOT smart care

experiment results based on the above report, it was

found that 20 graduate students in this laboratory, 2

of whom were often restless. And in the experiment,

each person sat an average of 3.52 hours, went to the

toilet 3.4 times, filled water 2.31 times and walked

31.63 meters. If some students have abnormal day

activity data in the future, we can take care of his

health immediately. Through more tag positioning

and access to historical data of the device, such as

“Figure 17”. We collected tens of thousands of wearer

device mac address, contact device mac address,

corresponding event actions, positioning coordinates,

event time and other data in each experiment, more

detailed behavior patterns can be analyzed as shown

in “Figure 18”. When elderly people living alone have

irregular lifestyle habits, they can ask for care or

notify the family before the tragedy occurs.

CONCLUSION

This study is dedicated to improving quality of

people's life. The only solution to deal with and

manage the behavior and health of millions of people

is big data and Internet of Things technology (Kwok

Tai Chui et al., 2019). We hope to use any simple

BLE device to automatically sense the surrounding

environment and build a smart IOT care system.

Therefore, this research uses BLE as the main axis to

design, which includes wearable devices, Google

Home Nest Mini, magnetic reed switches and other

devices. We integrate these with cloud database and

big data analysis system. Since the system in this

experiment can collect basic sensor data, at the same

time, it can collect historical data of absolute

positioning of personnel.

Where detailed people go in a day? What action

was taken? What time does the elder do with other

elders or caregivers in the living room or social room

every day? Long-term big data analysis can reveal

more detailed life pattern data, and even the social

relationships of the elders.

Although we have designed whole system and

done some testing, we have still not finished the

Google Home System experiment. But we will still

work on it, design new data charts and use them to

make whole system better. In the near future, through

Google Home System, we will provide care,

reminders, suggestions and other levels of treatment

to elderly proactively.

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