Issues in Data Fusion for Healthcare Monitoring - Semantic Scholar

Issues in Data Fusion for Healthcare Monitoring Hyun Lee

Computer Science and Engineering The University of Texas at Arlington Arlington, TX 76019

Kyungseo Park


Byoungyong Lee


[email protected] [email protected] [email protected] Jaesung Choi Ramez Elmasri Computer Science and Engineering The University of Texas at Arlington Arlington, TX 76019

[email protected]


[email protected]

ABSTRACT

1.

Pervasive healthcare monitoring using body sensors and wireless sensor networks is a rapidly growing area in healthcare monitoring applications. Several issues arise in these systems, such as complex distributed data processing, data fusion, unreliable data communication, and uncertainty of data analysis in order to successfully monitor patients in real time. In this paper, we introduce some of the important issues in healthcare monitoring with focus on software problems such as reliability, network robustness, and context awareness. We describe related works in data filtering, data fusion, and data analysis then we suggest new architecture for handling data cleaning, data fusion, and context and knowledge generation using multi-tiered communication and a triadic hierarchical class analysis approach. The proposed architecture is called ”Pervasive Healthcare Architecture” and we discuss how it can be applied to a particular monitoring scenario.

In recent years, healthcare applications such as home-based care, disaster relief management, medical facility management and sports health management have gained considerable interest [14, 35, 30, 44]. A wide range of pervasive computing technologies that aim to provide services to people by using embedded intelligent systems can be applied to these scenarios. Using pervasive computing technologies for everyday healthcare management, a pervasive healthcare monitoring system enables real-time and continuous healthcare monitoring. In addition, automated diagnosis and treatment enable independent living, general wellness and remote disease management without spatial-temporal restrictions. Therefore, a pervasive healthcare monitoring system (PHMS) has advantages such as comprehensive health monitoring services, intelligent emergency management services, and self-adaptable automation services [19, 45, 38]. To obtain these advantages, a PHMS uses advanced technologies such as body sensor networks (BSN), wireless sensor networks (WSN), mobile devices (PDA, Handheld, cell-phone), and IT-based network (Bluetooth, Wifi, Zigbee, internet, etc) [48, 47, 12, 26].

Categories and Subject Descriptors J.3 [Life and Medical Sciences]: [medical information systems]; C.2.1 [Network Architecture and Design]: [Network Architecture and Design]

General Terms Design

Keywords Pervasive Healthcare Monitoring System, Data Fusion, Context Generation, Reliability Permission to make digital or hard copies of part or all of this work or personal or classroom use is granted without fee provided that copies are not made or distributed for profit or Permission to advantage make digitaland or hard copies of all this or part of this commercial that copies bear notice andwork the for personal or classroom usepage. is granted without fee provided that copies are full citation on the first To copy otherwise, to republish, not madeon or servers, distributedorfor or commercial and that copies to post to profit redistribute to lists,advantage, requires prior bear this notice and theand/or full citation specific permission a fee.on the first page. To copy otherwise, to republish, to post servers2008, or to redistribute to lists, requires prior specific PETRA'08, Julyon15-19, Athens, Greece. permission a fee. 978-1-60558-067-8... $5.00 Copyrightand/or 2008 ACM PETRA 2008, July 15-19, 2008, Athens, Greece. Copyright 2008 ACM X-XXXXX-XXX-X/XX/XX $5.00.

INTRODUCTION

A PHMS approach is composed of the combination of four components as shown in Figure 1. First, medical sensor systems continuously collect sensed data from a person/patient and the environment. Second, the information system aggregates and combines data by applying data fusion techniques in order to generate a context for a particular situation. Finally, a doctor analyzes the situation based on analysis tools and provides feedback to the person/patient. If an emergency situation occurs, medical emergency aid can be requested by a doctor or an emergency alarm system. Motivation: Many issues still exist in the pervasive healthcare area. According to [11, 41], issues can be classified into five categories (Hardware, Software, Regulations, Standardization, and Organization). Our focus in this paper is on software issues (Reliability, Context Awareness, and Actuation). The first issue is how to gather useful data from

2.1 Body Sensors Data Collection Elderly Person & Patient Feedback & Treatment

Smart Actuation Monitoring & Detection

Management

Medical Sensor Networks Aggregation & Fusion

Adaptation Doctor & Emergency

Data Analysis & Knowledge

Information Systems

Figure 1: A Pervasive Healthcare Approach body sensor networks and environment sensing devices in real time. The rate of collected data is high in medical sensor networks and is continuously increasing as new measurements are taken over time. Thus an efficient data cleaning process is necessary in the data collection step to identify and keep relevant data summaries. The second issue is how to generate a reliable context in the information system using data aggregation and data fusion. Different aggregation and fusion techniques may need to be applied depending on the types of sensed data. In addition, reliable and robust communication is necessary in the data fusion step. The goal is to produce high confidence data for medical diagnosis and treatment. This leads to a high belief level in the generated context. Our approach: We propose a ”Pervasive Healthcare Architecture” (PHA) that focuses on resolving the software issues discussed above. The proposed PHA incorporates data cleaning methods such as synchronization, sampling and quick response in emergency situation, to handle the real-time emergency aspects. Context generation methods such as interoperable and multi-tiered communication in combined sensors (Physical, Space, Time, and Environment) are used to generate accurate contexts. Using a triadic approach such as hierarchical class analysis, we may resolve the last issue of producing high belief contexts.

In pervasive healthcare applications, body sensor devices play an important role to obtain information of an elderly person or a patient’s body condition. Broadly, we divide body sensor devices into two classes: internal sensor devices and external sensor devices.

2.1.1

Internal Sensor Devices

For internal types of body sensors, ingestible capsule and implanted sensors can be used. For instance, a core temperature sensor embedded in an ingestible capsule that is easy to swallow can measure a core body temperature (CBT). In [8, 1], they introduce a new product for checking the core body temperature. Also, some implants sensor can check medical information by using implant chip. These may diagnose conditions such as Parkinson’s disease and paralysis [7, 4]. The VeriChip [7] is a small RFID chip sized grain of rice that is implanted under the skin. Neural Signals also have products for Parkinson’s disease [4]. In addition, Endoscope sensor that is swallowed by patient measures internal body conditions by using various kinds of information. RF System Lab implements two products: Norika3 (2001) and Sayaka (2005) [6].

2.1.2

External sensor device

External types of body sensors include wearable and detachable electrical signaling devices. For example, Pulse Oximeter sensor measures heart rate (HR) and blood oxygen saturation. For blood oxygen saturation, the sensor detects colors of beams based on hemoglobin molecules. The sensor uses two beams on finger or earlobe then calculates the amount of beam reflected by hemoglobin. Moreover, a HR is measured by contracting and expanding of blood vessels. There are several products [10, 5, 3, 39]. Also, Electrocardiograph sensor checks the cardiac information. Sensors detect cardiac rhythm then electrocardiograph obtains the information signal from the contraction and extension of the cardiac muscle. Products of these types of sensors are discussed in [9, 20]. In addition, skin temperatures are detected by dermal body temperature sensor patched as introduced in [8, 21].

Medical Body Sensors

The rest of this paper is organized as follows. Types of sensors and sensed data in healthcare monitoring are briefly introduced in section 2. In section 3, we broadly discuss issues with data collection, data fusion and data analysis. We propose the ”Pervasive Healthcare Architecture” in section 4. Finally, we conclude the paper in section 5.

2.

TYPES OF SENSORS AND SENSED DATA IN HEALTHCARE MONITORING

In general, several types of sensors such as medical body sensors, environmental sensors and actuators, location sensors, and time stamps are used in pervasive healthcare monitoring system. We show the relationship between these types of sensors in terms of distance between their locations and the position of a patient in Figure 2.

Environmental Sensors & Actuators

Location Sensors & Time stamp

Figure 2: Types of Sensors

2.2

Environmental Sensors and Actuators

Environmental variables are also important factors in pervasive healthcare system. Environmental sensing data is combined with sensed body/location/time data to assist the analysis of personal situations. In addition, decision making is enhanced by operating smart actuators or providing feedback requests of a doctor. Thus parameters such as temperature, humidity, air quality, illumination, and noise are calculated and applied to the current PHMS. For instance, assume that elderly person enters the bathroom. We can check the physiological condition using body sensors and find the location using location sensors. However, we can not easily distinguish a cardiac episode from a period of staying in a hot tub unless location information is provided, since both situations can cause rapid heartbeat and degenerated ECG signal. In this case, temperature and humidity sensors can help to analyze the current situation and an acoustic sensor can trace the activity of the elderly person to make a context. In addition, a pre-installed actuator can reduce the CO2 level and can control the lighting level based on the air quality and illumination sensor readings to support better conditions. Therefore, it is important to include environmental types of sensors for determining a reliable context generation and a better quality of service. A dimmable lighting, fire alarm, flood alarm, heater, ventilate, and airconditioning are examples of systems that can be controlled by various types of actuators.

2.3

Location

Spatial information of single or multiple persons is one of most important factors to check the targeted person’s condition in a PHMS. In the healthcare monitoring area, the system has to keep tracking the person’s location, because, depending on the location, a body condition of a body sensor worn person might be changed. For example, when the person exercises in a fitness room, his heart rate and body temperature can be increased and these changes of body condition are normal. If the PHMS is not location-aware, the system might falsely warn to a medical institution. Therefore accurate location information is a momentous factor to make a correct decision of the need for emergency aid. The most uncomplicated solution is that each healthcare needing person uses a Global Positioning System (GPS) receiver, and a home gateway system intercommunicates with the GPS receiver to track the person. However, the GPS is not a practical solution because GPS is limited in indoor circumstances and accuracy of this system is not stable due to the several sources of error such as ionospheric effects, ephemeris errors, and satellite clock errors [2]. Therefore, relative localization in the indoor healthcare area is required among the sensor nodes in a pre-specified space, for example, home arrangement and furniture places. For high accuracy, the home gateway device has to pre-acquire localized sensors’ precise location, and it maps the targeted person’s spatial information based on the sensed data into the prespecified space [32]. Types of on-board sensing equipment include acoustic, InfraRed, pressure, and camera. With the use of sensed data, the location of a person can be processed by triangulation, trilateration, or multilateration approaches [40]. Moreover, active RFID based localization schemes can be employed in

the indoor health care system such as LANDMARC [33] and its variation [49].

2.4

Time

Time can give medical doctors or caregivers valuable temporal information to treat patients appropriately. Moreover, in order to generate correct context from the given sensors that we mentioned so far, each type of sensor has to have a function to record the timestamp on each data packet of sensed data. Timestamps along with sensed data from different types of sensors play an important role in analyzing a situation. Dey et. al. specify time as one of the primary context types for characterizing a particular situation with other context types, including location, identity, and activity [16]. For example, if location sensors detect a patient in a living room staying for several hours, then the system can generate different context based on whether it was at 2:00 am or 2:00 pm. In our proposed architecture, we assume that a function for timestamp generation exists in all the types of sensors: body, environmental, and location. This creates an additional issue of clock synchronization among the various sensors, but we do not discuss this issue here.

3.

ISSUES WITH DATA COLLECTION, DATA FUSION, AND DATA ANALYSIS

In this section, we describe related approaches about issues such as reliability, identification of patients, data management, and context and knowledge generation. Then we introduce our suggestion to resolve these issues.

3.1

Reliability Issues

Reliability in a PHMS is critical in that the system has to report correct data in a timely manner to a doctor or someone who is responsible for patients’ health condition. Issues of reliability can be classified into three main categories: reliable data measurement, reliable data communication, and reliable data analysis.

3.1.1

Reliable Data Measurement

As we mentioned in 2.1, there are two types of body sensors, internal and external. The first issue of reliability is on the correctness of measured data by body sensors. Internally implanted sensors, such as a retina or cortical prosthesis, can heat the surrounding tissues by generating radiation from wireless communication and power dissipation of sensors [42]. This thermal effect by tissue heating can cause incorrect data measurement from the sensors and may harm the tissues. In order to minimize this effect, Tang et. al. [42] proposed a rotating leadership algorithm within a cluster that considers the leadership history and the sensor locations. Also, they proposed [43] a thermal-aware routing algorithm that avoids the region whose temperature is above average and hotspot whose temperature exceeds a predetermined threshold value, where multi-hop sensor networks are necessary. For the reliable measurement of external sensors, we can add an extra redundant sensor for the same function in case of a malfunction. A body sensor reliability check module gathers two values from two sensors that measure the same function and compares them to determine if there is any error based on a predetermined error bound. The module analyzes the

data temporally so that it can detect any time-related outlier value. This is done in real time and if there is any error detected, the module can ask corresponding sensors to send current reading again. All the data that pass reliability check can be passed to the next step, which is data fusion and context generation.

3.1.2

Reliable Data Communication

Most PHMSs adopt wireless communication, since it is useful to gather data anytime and anywhere. But, due to the intrinsic properties of wireless medium, such as signal attenuation and distortion, it is not reliable to transmit data in wireless networks. Data or packet delivery rate is even decreased when the network uses multi-hop communication, such as ad-hoc wireless networks or wireless sensor networks. One of the possible solutions for reliable data communication is to have multiple wireless networks, such as wireless sensor networks, ad-hoc wireless networks, cellular networks, satellite networks, and wireless LANs, as proposed by Varshney in [46]. This architecture can utilize characteristics of each network, such as in terms of bandwidth, coverage, required power level, and priorities for access and transmission, whenever the characteristic is necessary. But, for an architecture with non-homogeneous networks, specific hardware and protocols are needed to switch from one network to the other. In addition, intelligent devices that can detect different types of networks, and an algorithm to choose the best network available are also needed. Instead of having multiple non-homogenous networks, we can propose multiple homogenous networks. As mentioned in 3.1.1, multiple sets of sensors can send their data through multiple homogeneous networks, and the data are eventually gathered by the body sensor module, the environmental sensor module and location sensor module. In this case, data delivery conflict by the same function sensors can be avoided by using intentional transmission delay at sensors or delayed measurement in different sets of sensors. Moreover, in order to increase reliability in one network, we can use multi-path routing that sends redundant data to increase robustness or conditional multi-casting and h-distance common ancestor tree algorithm where in-network aggregation is required [15].

3.1.3

Reliable Data Analysis

Once the data are measured and transmitted to a server correctly, the system has to analyze the data so that it can generate appropriate context. For reliable data analysis, as we will describe in section 3.4, the reliability in context generation is important in that the result of it can assist medical doctors to make the correct decision. Our proposed architecture has related modules for reliable data analysis. The data fusion module has a hierarchical decomposition method, and the context and knowledge generation module uses a modified triadic context of hierarchical class analysis, which is originally proposed by Hwang et. al. in [24].

3.2

Identification of Patients

Identification of targeted person and variable sensors provides more reliable and accurate healthcare monitoring which is based on wireless communication. A PHMS might serve multiple persons in a particular space, and there is flooding of sensed data from a large number of heterogeneous sensors

and people. The precise identification is mandatory to avoid ambiguousness of source of sensed data and to classify the collected data depending on identification of person. Identification is also needed in the localization and tracking of the patient. Each of the body sensors has to be identified because the system has to know which body sensor belongs to whom. In a PHMS, we propose a multi-session based passive High Frequency RFID system to achieve better reliability and accuracy of identification. We assume each patient patches a CLASS 5 active tag based badge which has a functionality of tag reading. Each internal and external body sensor tags CLASS 2 High Frequency RFID tag which is readable 3 ft from a reader. The badge identifies and gathers ID of the body sensor periodically, and then transmits own ID and collected IDs to the home gateway. Moreover, the passive tag maintains the session ID which is randomly chosen by the reader (the badge), and the reader communicates with only the selected session’s tags which are placed the single patient. Even though, the patient replaces one of body sensors due to some of reasons, the system can identify the replaced sensor in real-time.

3.3 3.3.1

Data Management Data Cleaning and Summarizing of Data

In a PHMS, accuracy of sensed data from patients is very important. If we obtain uncertain data from the sensors, it causes a critical situation for the patient’s well-being. Unfortunately, sensors have several factors that affect data accuracy, such as environment effects and hardware problems [17, 34]. For example, a patient’s body motion creates friction and intermittent connection with the sensor [21]. Thus data cleaning such as sampling and filtering is a crucial part of pervasive healthcare sensor networks. The place for data cleaning is divided into two locations: the sensor and the base station. Many researches proposed methods to reduce the uncertainty. In [17], Bayesian approach is used for combining prior knowledge of true sensor readings, the noise characteristics of sensors, and the observed noisy reading. In [25], a framework for building sensor data cleaning is proposed for pervasive healthcare application.

3.3.2

Storage of Data

After a body sensor generates a measurement, it transmits sensed data to the base station. The base station can be located in an area of the patient’s home because of the short radio range of sensors. After data cleaning and summarizing, it is stored in the database of the base station. The database will store all kinds of data such as sensed body/environment/ location/time data. Thus it will need a well organized storage system. In addition, a server, which receives data from the base station, is located in a medical institution. Then a doctor can check the patient condition through the server. The server increases network traffic and storage resource consumption since the server manages many base stations. Thus we propose that the base station generally only sends abnormal patient conditions such as urgent signaling or abnormal data. However, when a doctor requires all information from a base station, the base station sends all the patient’s data for further analysis, diagnosis and treatment.

3.4

Context and Knowledge Generation

Table 1: Example of Location (K3 ) Time (K2 ) a B-Sensor1 (K1 ) x E-Sensor1 (K1 ) E-Sensor2 (K1 ) x E-Sensor3 (K1 )

For accurate context and knowledge generation, data fusion, decision making, and context awareness are required.

3.4.1

Data Fusion

Generally, data fusion is the process of putting together information obtained from many heterogeneous sensors, on many platforms, into a single composite picture of the environment. Also, fusion of data from multiple sensors is useful for obtaining more reliable information than individual measurements obtained from a single type of sensor [23, 28]. In a PHMS, three levels of data fusion are employed. Raw sensor data fusion provides better information at raw level. Feature level fusion finds relevant features among various features coming from different methods. Decision level fusion combines decisions or confidence levels coming from several experts [31]. In addition, there are some challenges such as sensor or information normalization, estimation of parameters, dynamic optimization, data pre-processing for extraction, and optimal algorithms [22, 18]. The main issue in data fusion is to provide higher accuracy and improved robustness against uncertainty and unreliable integration. In the proposed PHA, we suggest a hierarchical decomposing method as shown in Figure 3, to minimize the probability of unacceptable error. Body sensor networks eliminate overlap function by decomposing types of sensors. Environmental sensors and actuators reduce the redundant data by using data fragments that can describe the phenomenon. Based on the location, these data fragments have different feature extraction. Then we estimate some pattern depending on the time variables.

context B b c x x x x

x x

K1, K2, K3 and a ternary relation Y between them. The elements of K1, K2, and K3 are called two types of sensors (body sensor and environmental sensor), time and location respectively in PHMS. Table 1 shows an example of a triadic context for a PHMS. Based on Table 1, the equivalent sensors (location, time) can be grouped together into a set (class) as shown in Figure 4 (a). Based on the relation of three elements, the hierarchies of the sensors, location, and time class can be integrated into a triadic class hierarchy as shown in Figure 4 (b). Then the knowledge is extracted from a triadic hierarchical class analyzer. Finally, the doctor can make a diagnosis and treatment by analyzing the triadic class hierarchy.

(a) Extract equivalent sets

(b) Build a triadic class hierarchy

B1 E1

E2

B1 E3

b Time

a triadic A b c a x x x x x x

a

Pattern Processing

E1

E2

E3

A

A,B

B

a

b

c

c A

Location Identification Feature Extraction Environmental Sensors and actuators

B Data Fragments

Medical Body Sensor Functional Decomposing

Figure 4: A triadic hierarchical class analysis for PHMS

3.4.3 Figure 3: Data fusion model for pervasive healthcare

3.4.2

Knowledge

Decision Making and Data Analysis

A context [37] is defined as a pattern of behavior or relations among variables data that potentially affect user behavior and system performance. A context is derived from fused data in combination with a decision making policy. Several types of decision-making techniques [36] are used in healthcare systems. For instance, probabilistic model (Bayesian reasoning, Belief network and Markov Decision Process), least squares model (Kalman filtering) and intelligent fusion model (fuzzy logic, neural network, genetic algorithm and reinforcement learning) are used. However, in this paper, we suggest a different approach by modifying a triadic context of hierarchical class analysis [24]. A triadic context K is a quadruple (K1, K2, K3, Y) that is composed of three sets

Context Awareness and Actuation

In pervasive healthcare, context-aware computing [27] is adapted to changing circumstances and responses based on the context of use. Also, according to [13], many researchers already focus on context-aware computing in pervasive healthcare applications. However, there are still issues with context awareness. Problems such as the functional needs of the context, the gap between fundamental context representation and actual context awareness prototype, and the difficulties in building an intelligent system to mimic human perspective still exist. In the next section, we show a possible ”Pervasive Healthcare Architecture” for monitoring with some scenarios to resolve these issues. Furthermore, we suggest an intelligent system [29] which is able to control the environmental variables by adapting the generated context. Two actuators such as lighting system and heating, ventilating, and air-conditioning (HVAC) are operated at each location to make optimal values for patients. An alarm ac-

Alarm Module

Data Collection Module

Body Sensor

Environmental Sensor

Spatial Sensor

Time Synchronization

combined with decision-making policy and the reliable data analysis is checked by an analytical method in the context and knowledge generation module. To save her disease history, sensed raw data and the monitoring results are kept in storage located in her house. Finally, a doctor regularly monitors Alice’s history and real time variations to provide feedback to Alice. Thus Alice is able to manage her disease continuously even if she does not go to a hospital.

4.2 Data B-data filtering E-data filtering S-data filtering Cleaning Data Reliable Measurement Check Module

B-Aggregation Data Fusion Module

S-Aggregation

Data Fusion Layer Data Reliable Communication Check

Context & Knowledge Decision Generation Making Policy Module

Server Module

E-data aggregation

Context Generation Layer Reliable Analysis Check Knowledge Generation

Medical Institution Server Whole Patients’ Histories Storage

Personal History Storage

Emergency Call Management

Figure 5: A Pervasive Healthcare Architecture tuator also sends an emergency call to the doctor and the medical institution based on the pre-defined criteria.

4.

PROPOSED PERVASIVE HEALTHCARE ARCHITECTURE (PHA)

In this section, first, we present one scenario of pervasive healthcare monitoring. Then we propose a ”Pervasive Healthcare Architecture” and describe it based on the applied scenario.

4.1

Applied Scenario

Alice has cardiac disease so she attaches body sensors into her body to check body temperature, blood pressure, and heart beat rate. She sets up environmental sensors and actuators in each room to monitor surrounding variables, to support a comfortable situation for her, and to provide additional information to the doctor or the medical institution. Also, she can be identified by location sensors wherever she stays. Moreover, she can receive the optimal operation of actuators for preventing an urgent cardiac disease. If body sensors and environmental sensors catch a cardiac symptom, the emergent signaling is processed by an alarm module. In general, Alice is just monitored by the attending physician. To implement this, first, sensed data is filtered at data cleaning module located in the information system. Also, the data reliability measurement is checked in the data cleaning module. Second, filtered data is aggregated and fused in the data fusion module and the reliable data communication is checked in the data fusion module. Third, fused data is

Proposed PHA

Based on the applied scenario, we proposed a PHA as shown in Figure 5. The PHA broadly consists of three parts: sensor devices, Information system, and a medical institution. In sensor devices, each sensor collects data then sends sensed data to the information system using wireless communication. In information system, three separate modules such as data cleaning module, data fusion module, and context and knowledge generation module operate to make reliability. In medical institution module, a server which has a storage module, emergency call management module, and a monitoring service module is operated. Moreover, an alarm module is connected to information system to give the information to patients and the medical institution.

5.

CONCLUSIONS AND FUTURE WORK

In this paper, we discussed several issues in pervasive healthcare monitoring applications. Especially we deal with software issues such as reliability of measurement, location identification, and network robustness for guaranteeing the context accuracy and for reducing a false positive diagnosis from a doctor. We discussed the importance of data collection, data cleaning, data fusion, context and knowledge generation, and data analysis. Finally, we propose a PHA scenario which applies our suggestion into the system. In the future, we will consider the detailed limitations for data collection module for eliminating a false positive as well as a false negative identification in a PHMS. Also, we will consider trade offs between data cleaning layer and data fusion layer for increasing data efficiency and data reliability.

6.

REFERENCES

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