Contextual activity based Healthcare Internet of Things, Services, and People (HIoTSP): An architectural framework for healthcare monitoring using wearable sensors
Introduction
The integration of wireless communication, sensor, and information technology has opened new paradigms in the field of wellbeing and healthcare management. It enables the quality of life and extends the autonomous living of individuals at home. Today, embedded sensors can measure a wide range of physical parameters with the minimal complexity. This phenomenon becomes realistic due to their low power consumption and cheaper production cost. These embedded sensors can be integrated in small wearable devices like watches, pendants, and clothes. Moreover, they can also be applied to everyday objects or places, such as appliances, furniture, or the like, for automation purposes. Internet of Things (IoT) has radically changed the way the human interacts with things, i.e. sensors, objects, services, and applications. These entities provide continuous data streaming over a protracted period of time, based on its characteristics. The acquired data from these diversified set of sensors can be analyzed and used to enable a service or process an action based on the user's request [1], [2], [3]. Since IoT is not bounded to any particular field, it has been applied to a wide range of services [1], [3], [4], [5].
IoT has profoundly changed the way of healthcare service delivery, and as a result, physicians recommend the use of health-related applications to patients [6]. Many applications are available which take input from the users about their daily routines and dietary habits [7], [8] to report their health condition, but IoT driven healthcare can benefit from the automatic collection of information through internal and external sensors for detecting anomalous conditions. These sensors refer to the built-in sensors in smartphones [9] and wearable sensors like smartwatches [10]. IoT has been extensively studied for smart homes. The related services are based on the changes in sensor readings such as humidity, temperature, ambient light sensors, and so forth. The changes in the sensor measurements are used to control the home appliances and enhance the quality of living without user intervention. Researchers are still trying to assess the complete spectrum of IoT driven healthcare systems. There is a dire need for powerful tools that can acquire data from multiple sensor modalities such as psychological, physiological, and inertial data to validate the autonomous healthcare monitoring system.
Some studies have stressed on the acquisition of multimodal data [11] whereas others focus on analyzing the provenance of the acquired data [12]. Healthcare IoT based services are mainly implemented with inertial measurement units (IMUs) for activity recognition, event/process detection using object/location sensors, and physical state monitoring using physiological sensors [13], [14]. Most of the works have been performed using event/process detection with object sensors and activity recognition. These works are specifically proposed for either inhouse patients, elders or people with specific disease which requires a proper setup or infrastructure to be deployed, hence, trading off with mobility and easiness of such systems. Moreover, the use of physiological sensors has not been exploited to its full potential yet in the field of healthcare driven IoT services. It is apparent that more usage of sensors results in wider scope of the health-related services. In this regard, we propose a framework which can combine multiple sensor modalities to provide the healthcare services in an accurate and an efficient way for all users. The use of wearable sensors in this framework allows the system to be location-independent or infrastructure-less system.
In general, IoT is a four-layered architecture with sensing, network/access, service and interface layer [1], [2], [3], [4], [5], [15], [16]. The sensing layer is responsible for the integration of different sensors, their connection to the physical world, and collection of data. The network/access layer provides the networking support and is responsible for the transfer of the data in wireless or wired networks. The service layer is managing and creating services as per the user's requirements. The interface layer is commonly used for presenting the analyzed data or the output of the desired services. The users or associated entities are able to interact with the IoT system using the interface layer. In general, IoT framework with specified layers is limited to some specific services but including another layer such as context recognizer can extend the scope of healthcare driven IoT and can be used for the automation of services. The important requirements for designing IoT based healthcare monitoring framework are summed up as follows [1], [2], [3], [4], [5], [15], [16]:
- •
Holistic solution: healthcare driven IoT can be used for physical fitness, safety, and health, as this solution encompasses everyone needs.
- •
Fusion of technologies: healthcare driven IoT can be integrated with a wide variety of sensors and can support multi-modal sensor platforms.
- •
Analytics and BigData: with recent advancement in cloud computing technology, multi-modal, multi-scale, and heterogeneous data can be easily pre-processed and analyzed in a reasonable time. This allows the applicability of real-time systems for healthcare services.
- •
Personalized services: analytics and big data techniques such as machine learning and recommendation systems expand the possibility for personalized healthcare treatments and services. It can also help early detection of anomalous conditions.
- •
Data for many time-lines: users can receive or access their past, present, and future predicted data anytime and anywhere.
- •
Telehealth features: the data and output from desired services can be shared with doctors in real-time. Moreover, doctors can monitor larger number of patients with the use of healthcare driven IoT systems.
In accordance with the requirements above, in this paper, we introduce the healthcare driven Internet of Things, services, and people (HIoTSP) framework in the context of wellbeing and healthcare using smartphone and wearable devices as shown in Fig. 1. HIoTSP is the interconnection of things (sensors), services (application and service triggering based on analysis) and people (caregivers or doctors) via the Internet for healthcare services [17]. This framework integrates the use of inertial, physiological, and location sensors for our context recognizer for deriving contextual activity to automate the service selection process. The sensor layer in HIoTSP is responsible for acquiring measurements from various sensor modalities. These sensor measurements are then transferred to the middleware via the access and communication layer which provide the means for interfacing. The middleware in the proposed framework is the smartphone which acts as a gateway for sending and receiving the information to and from the server layer. It can also perform some lightweight operations with the data stored in the buffer. The data received from the middleware is then accumulated in the storage of server layer. The server layer uses the data to recognize contextual activity to select the desired service(s), respectively. The data in compliant to the selected service is then passed to the service selection block for performing further analysis and decision making. This data is also used for summarizing and recording the event logs. The decision from the service selection is periodically checked by the middleware. As the decision is available, it is fetched by the middleware and immediately relayed to the notification center for alert generation and recommendations (the details of each of the blocks are provided in Section 3).
The proposed framework supports mobility as it can be used indoors as well as outdoors, low cost as it uses only wearable sensors and common devices such as smartphones, and user-friendliness as devices are unobtrusive, less complex, and easy to operate. The framework uses ontological reasoning or inference rules to select an application or service for analyzing, summarizing, and visualizing the data. The interface layer (notification center) in this framework explicitly deals with generating declarations based on the attained results. We have also implemented two health-related services i.e. fall and stress detection which are considered to be major anomalous conditions in the literature. The current implementation of the framework is geared towards research and small-scale field tests rather than towards the usage in practice with patients and caregivers. The contributions of the proposed work are summarized as follows:
- •
Integrated framework for healthcare driven IoT services that includes semantics (context recognizer) layer
- •
Combining multiple sensor modalities to automate the service selection as well as to cover wide range of health monitoring services.
- •
In-depth analysis for contextual activity recognition and related services such as fall and stress detection.
- •
Real-time implementation and validation of the proposed HIoTSP framework.
The rest of the paper is structured as follows, Section 2 highlights the existing studies and frameworks; Section 3 presents the proposed architecture of our framework. Section 4 provides the details for the implementation details of the framework and services. Section 5 explains the results from our implementation of framework modules and services. Finally, the discussion and conclusion are presented in Section 6.
Section snippets
Related works
This section carries out a literature survey of IoT driven healthcare systems. Though a lot of research works have been conducted on IoT based healthcare systems, the ones which are closely related to our scope are discussed in this section.
Niranjana and Balamurugan presented an intelligent home-based healthcare IoT system using a medical box (iMedBox) and iGate which works as a home healthcare gateway. Wearable sensors are coupled to the iMedBox via RFID link to monitor the user's health [18].
Proposed framework
The proposed HIoTSP framework is developed for health monitoring of individuals and is shown in Fig. 1. The framework is comprised of the dependency and interconnectivity of wearable sensors with applications and services through smartphone. This framework is designed in such a way that it could support mobility, low cost, and user-friendly perspectives. Caregivers, relatives, and doctors may have the opportunity not only to monitor the current health condition of an individual but also to get
Implementation using HIoTSP framework
We developed an Android application and a cloud server for the implementation of the HIoTSP framework. The application has been tested on Android operating system from version 4.3 (“Jelly Bean”) to onwards. The minimum set of sensor requirements for HIoTSP framework comprises location, inertial and physiological sensors. The current implementation uses Estimotes as location sensors, Microsoft Band 2 and Samsung Galaxy (Note5 and S7) for physiological and inertial sensors. The reason for
Results
This section presents the results for the implicit service (contextual activity recognition), and selectable services (fall and stress detection).
Conclusion and future works
The characteristics of healthcare provision have been transformed from clinic-centric to service-centric healthcare. This transformation has been mainly led by the increasing development in IoT technology, which provides seamless connection of cloud storage and devices to patients, individuals, healthcare services, hospitals, and so forth. In this paper, we have proposed the HIoTSP framework that is designed to collect data from wearable sensors, which not only monitors the health of an
Acknowledgment
This research is partially supported by Basic Science Research Program through National Research Foundation of Korea (NRF) funded by the ministry of Education (2015R1DA1A01061402) and Institute for Information and Communications Technology Promotion (IITP) grant funded by the Korea government (MSIP) (No. B0113-15-0002, Development of Self-Learning Smart Ageing Service based on Web Objects).
Sunder Ali Khowaja received the M.E. degree in communication systems and networks from Mehran University of Engineering and Technology, Jamshoro, Pakistan, in 2014. He is currently pursuing the Ph.D. degree in Industrial and Information Systems Engineering with the Hankuk University of Foreign Studies, South Korea. His research interests include Data Analytics and Machine Learning for Affective Computing, Ambient Intelligence and Computer Vision applications.
References (73)
- et al.
Internet of things (IoT): a vision, architectural elements, and future directions
Future Gener. Comput. Syst.
(2013) - et al.
The internet of things: a survey
Comput. Netw.
(2010) - et al.
Internet of things: vision, applications and research challenges
Ad Hoc Netw.
(2012) - et al.
Towards fog-driven IoT eHealth: promises and challenges of IoT in medicine and healthcare
Future Gener. Comput. Syst.
(2018) - et al.
Cloud-assisted industrial internet of things (IIoT) – enabled framework for health monitoring
Comput. Netw.
(2016) - et al.
An IoT-cloud based wearable ECG monitoring system for smart healthcare
J. Med. Syst.
(2016) - et al.
Hierarchical classification method based on selective learning of slacked hierarchy for activity recognition systems
Expert Syst. Appl.
(2017) - et al.
iKnow: ontology-driven situational awareness for the recognition of activities of daily living
Pervasive Mob. Comput.
(2017) - et al.
MetaQ: a knowledge-driven framework for context-aware activity recognition combining SPARQL and OWL 2 activity patterns
Pervasive Mob. Comput.
(2016) - et al.
Probabilistic ontology based activity recognition in smart homes using Markov logic network
Knowl. Based Syst.
(2017)
The CART decision tree for mining data streams
Inf. Sci.
A decision-theoretic generalization of on-line learning and an application to boosting
J. Comput. Syst. Sci.
Cross-person activity recognition using reduced kernel extreme learning machine
Neural Netw.
Comparing stress of car and train commuters
Transp. Res. Part F Traffic Psychol. Behav.
Internet of things in industries: a survey
IEEE Trans. Ind. Inf.
Internet of things: applications and challenges in technology and standardization
Wirel. Pers. Commun.
Smartphone-based solutions for fall detection and prevention: challenges and open issues
Sensors
A wearable smartphone-based platform for real-time cardiovascular disease detection via electrocardiogram processing
IEEE Trans. Inf. Technol. Biomed.
A mobile data collection platform for mental health research
Pers. Ubiquitous Comput.
Provenance framework for mHealth
An IoT-based computational framework for healthcare monitoring in mobile environments
Sensors
COSAR: hybrid reasoning for context-aware activity recognition
Pers. Ubiquitous Comput.
Service-Generated big data and big data-as-a-service: an overview
An architectural approach towards the future internet of things
Intelligent E-health gateway based ubiquitous healthcare systems in internet of things
Int. J. Sci. Eng. Appl. Sci. (IJSEAS)
Internet of things (IoT) framework for u-healthcare system
Int. J. Smart Home
The potential of internet of m-health things “m-IoT” for non-invasive glucose level sensing
Around the clock personalized heart monitoring using smart phones
mHealthDroid: a novel framework for agile development of mobile health applications
Health care monitoring system in internet of things (IoT) by using RFID
Ubi-Liven: a human-centric safe and secure framework of ubiquitous living environments for the elderly
Temporal informative analysis in smart-ICU monitoring: M-healthcare perspective
J. Med. Syst.
A health-IoT platform based on the integration of intelligent packaging, unobtrusive bio-sensor, and intelligent medicine box
IEEE Trans. Ind. Inform.
Cited by (54)
Plug-and-play multi-dimensional attention module for accurate Human Activity Recognition
2024, Computer NetworksUnleashing the power of internet of things and blockchain: A comprehensive analysis and future directions
2024, Internet of Things and Cyber-Physical SystemsOptimization assisted bidirectional gated recurrent unit for healthcare monitoring system in big-data
2023, Applied Soft ComputingInternet of wearable things: Advancements and benefits from 6G technologies
2023, Future Generation Computer SystemsA systematic review of IoT in healthcare: Applications, techniques, and trends
2021, Journal of Network and Computer ApplicationsCitation Excerpt :This section provides an overview of the selected application-based approaches, including monitoring elderlies, recommending medicine and food, and other issues. Reviewing the articles showed that the articles focused on patients monitoring included prediction systems (Verma and Sood, 2018), (Kumar et al., 2018), (Sood and Mahajan, 2019), (Verma et al., 2018), (Kaur et al., 2019), (Suresh et al., 2019), (Tan and Halim, 2019), (Rajan et al., 2020), (Satpathy et al., 2019), (Bhatia et al., 2020), (Vedaraj and Ezhumalai, 2020), (Fouad et al., 2020), (Akhbarifar et al., 2020), detection systems (Azimiet al., 2017), (Jebadurai and Dinesh Peter, 2018), (Khowaja et al., 2018), (Tuliet al., 2020), (Alam et al., 2019), (Alhussein et al., 2018), (Rajan et al., 2020), (Ray et al., 2018), (Hosseinzadehet al., 2020b), (Zgheib et al., 2020), (Rahmani et al., 2020), (AbdulGhaffar et al., 2020), (Kesavan and Arumugam, 2020), (Kavitha and Ravikumar, 2020), and care-support systems (Jabbar et al., 2017), (Rahmaniet al., 2018), (Laplante et al., 2018), (Ramírez López et al., 2019), (Onasanya and Elshakankiri, 2019), (Rajan Jeyaraj and Nadar, 2019), (Laplante et al., 2018), (Jeong and Shin, 2018), (Ghasemi et al., 2019), (Yanget al., 2018), (Onasanya et al., 2019), (Vedaeiet al., 2020), (Sharma et al., 2020), (Bandopadhaya et al., 2020). Verma and Sood (2018) presented a cloud and IoT-based mobile-healthcare monitoring system for disease prediction.
Sunder Ali Khowaja received the M.E. degree in communication systems and networks from Mehran University of Engineering and Technology, Jamshoro, Pakistan, in 2014. He is currently pursuing the Ph.D. degree in Industrial and Information Systems Engineering with the Hankuk University of Foreign Studies, South Korea. His research interests include Data Analytics and Machine Learning for Affective Computing, Ambient Intelligence and Computer Vision applications.
Aria Ghora Prabono received the bachelor degree in computer science from Universitas Ma Chung, Indonesia, in 2015. Currently he is pursuing the Ph.D. degree in Industrial and Information Systems Engineering at Hankuk University of Foreign Studies, South Korea. His research interest is machine learning and its application on pervasive computing.
Feri Setiawan received the M.E. degree in Industrial and Information Systems Engineering from Hankuk University of Foreign Studies, South Korea, in 2018. He is currently pursuing the Ph.D. degree in Data Analytics at Hankuk University of Foreign Studies, South Korea. His research interests include Affective Computing, Ubiquitous Computing and Human Behavior Analysis.
Bernardo Nugroho Yahya received the Ph.D. degree in Industrial Engineering from Pusan National University; M.S. degree in Information System Engineering from Dongseo University; and B.S degree in Industrial Engineering from Petra Christian University. He is currently an Associate Professor in the Industrial & Management Engineering Department at Hankuk University of Foreign Studies, South Korea. Bernardo has been working on various industry business consulting and engineering projects with Korean companies. His current research includes Statistical Pattern Recognition, Machine Learning, Business Process Intelligence, and Data Analytics.
Seok-Lyong Lee is a professor in the School of Industrial and Management Engineering at Hankuk University of Foreign Studies. He received his Ph.D. in Information and Communication Engineering from Korea Advanced Institute of Science and Technology (KAIST). He received B.S. degree in Mechanical Engineering and M.S. degree in Industrial Engineering from Yonsei University. He was an Advisory S/W Engineer at IBM Korea from 1984 to 1995. His research interests include multimedia databases, image analysis, data mining and warehousing, and information retrieval.