A Cloud-Based Accessible Architecture for Large ... - Semantic Scholar

2011 IEEE 4th International Conference on Cloud Computing

A Cloud-based Accessible Architecture for Large-scale ADL Analysis Services Yu-Chiao Huang, Yu-Chieh Ho, Ching-Hu Lu, and Li-Chen Fu Dept. of Computer Science & Information, National Taiwan University Taipei, Taiwan. R.O.C. {r99922061, d97922024}@ntu.edu.tw, [email protected], [email protected]

automated architecture that facilitates the collection, maintenance, accessibility, and analysis of ADL information from massive users for context-aware applications such as commercial analysis, social trend prediction, energy saving, etc. In order to achieve aforementioned goal, we choose cloud computing due to its cost-effective and highlyelastic characteristics, which leads to increasing usage on the research fields of healthcare and pervasive computing. A traditional synergy of cloud computing and healthcare services is to upload real-time medical data from the monitoring sensors or the patient’s medical history to the cloud for long-term maintenance and analysis [4, 5]. Based on the cloud-tier data, care services are directly delivered to the mobile devices or indirectly through the caregivers who subscribe the analyzed information. This type of synergy is considered promising and is under-practicing by some medical care systems [4, 6]. Nevertheless, there is little cloud related work that concerns context-awareness, such as automatically long-term ADL data analysis. Therefore, we here propose a compound architecture that expedites the synergy of cloud computing and pervasive computing to the problems mentioned above. In the first place we will discuss the two main techniques involved, one for enhanced data acquisition through context-aware activity recognition (using pervasive computing) and the other for data maintenance as well as analysis (using cloud computing). The system as a whole delivers an ADL analysis cloud utility, which serves as an information provider for those applications that require the resultant analysis for their core services delivering to end users. At a higher abstraction, we aim for designing a platform where healthcare related applications can easily build upon and can readily access the cloud-enabled services. This paper is structured as follows: the next session discusses some related works from several perspectives of the proposed architecture. Section III explains the implementation of the two major phases of the proposed architecture in detail. A possible combination of a healthcare application embodiment is depicted in section IV and the last two sections delineate our evaluation and conclusion respectively.

Abstract ᧩ Recognizing Activities of Daily Living (ADL) plays an important role in healthcare. However, it is often impractical and sometimes impossible for a person to collect those useful data manually, not to mention constant long-term data maintenance and analysis. To address the above-mentioned challenges, we propose an architecture, in which many health-care applications and services can easily build upon, for collective long-term ADL pattern analysis that leverages several prominent advantages inherent in cloud computing. The core of the proposed infrastructure includes a module to perform MapReduce-assisted Bayesian activity recognition based on all collected ADL data. Better yet, the resultant data analysis can be delivered as a service from a service station which serves as a readily accessible interface to 3rd party service providers and endusers. For the evaluation of the proposed architecture, a simulation of persuasive health engagement is presented and discussed as one potential application.

I.

INTRODUCTION

As the population of the world keeps aging, it is surveyed by the World Health Organization (WHO) that chronic diseases have taken a large proportion of causes of death in medium and high income countries (8 out of 10) [1]. Since most chronic diseases, such as coronary heart disease and cancers, result from constant unhealthy living habits, recognizing and monitoring one’s Activities of Daily Lining (ADL) is increasingly important in healthcare. Traditional ways of collecting ADL data often need labor intensive approaches such as scaled questionnaires or in-depth interviews [2, 3], which is usually impractical and sometimes even impossible to put it into practice for long term analysis. Another critical problem is the post-processing of the collected data, including storage, maintenance, and analysis, this situation would become even worse when the collected data are scaled up due to the drastic increase in both time range and the number of observed participants. These gradually accumulated ADL data have to be effectively analyzed in order to extract useful information for knowing users’ long term habitual tendency and for modeling their users’ activity patterns. Therefore, we propose an effective way to analyze ADL data from a large mass of people such that the analyzed data can be readily shared with various healthcare related applications. In sum, our goal is the proposal of an 978-0-7695-4460-1/11 $26.00 © 2011 IEEE DOI 10.1109/CLOUD.2011.97

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II.

Nkosi and Mekuria proposed a cloud computing framework to mitigate the burden of mobile devices from executing heavy multimedia and security algorithms in delivering mobile health services by performing these operations in the cloud [17]. However, this framework did not store the sensory data collected from the users, which may be worth for further analysis. In addition, Khattak et al. proposed a system which achieving Ubiquitous Life Care by monitoring human health as well as activities [6, 18]. The core of the proposed system is human activity recognition engines. Different activity recognition engines were deployed on cloud to identify different set of activities of participants, after that, the results of recognition were stored and analyzed on cloud for healthcare related service provision. The services the system provides can be divided into two types: Service-by-long-term-observations (SLO) and Service–by-current-observations (SCO). The former is created from the analysis results of long term ADL records. In these works, however, the ADL data from different users are analysis independently. Valuable information, such as correlations between users and overall trend, cannot be discovered without multi-users, large scale data analysis. In our work, we proposed large-scale ADL analysis services from massive users to enrich the knowledge extract from ADL data.

RELATED WORK

In order to recognize ADLs, an activity recognition system shall be able to sense the environment. Generally speaking, the sensing approaches include demanding users to carry wearable sensors [7] installing cameras [8, 9], or deploying pervasive sensors [10, 11] (e.g. pressure sensors, reed switches) to collect important clues for later activity reasoning. In this work, we choose pervasive sensors and weave them with a “Hide and Not Easy to Seek” strategy [12] to maximize the comfort of the residents. The House_n project [13] is one of the pioneers achieving activity recognition using pervasive sensors. Tapia et al. deployed 77 sensors in a home environment, and applied Naïve Bayes classifiers to recognize several activities of the residents [14]. However, they used only two kinds of sensors which could limit the diversity of activities of interest. In this work, we make use of multimodal sensors to make the recognition more comprehensive and more accurate. Some researchers regarded activity recognition as an important assistive technology in medical practices. Tentori and Favela applied activity-awareness in the General Hospitals of Enseneda, Baja California, Mexico such that they can analyze the activities of patients and their caregivers. They classified them into three categories: (a) monitored activities: the activities which the hospital staff was supposed to monitor (on their patients) (b) distributed activity: a series of sequential activities which occurs in different locations and (c) dynamic activity: a set of activities which are switched frequently. All the information was shown in mobile devices to help the caregiver work efficiently [15]. On the other hand, Cynthia et al. measured the patient's activity by asking them to wearing wireless 3D accelerator. In this study, they noticed that the activity intensity of elderly patients was usually low, and lack of activity will lead to functional decline [16]. Previous studies had shown the importance of ADL analysis to healthcare. However, these studies focus on the ADLs in the hospital, which may be quite different with those in our regular life. Furthermore, the collected data and analysis mechanisms in these works are for nursing and academic use only, they are not accessible for other users. In this paper, we proposed a platform where healthcare related applications can easily build upon and can readily access the cloud-enabled services. In our literature survey, cloud computing is now considered as a suitable mechanism for ADL analysis in home environments, and for providing accessible healthcare services in one`s daily life. One of the important reasons is that cloud computing can enable huge cost savings and more efficiency in many public sectors, including hospitals and healthcare centers (especially for providing information and technology to remote locations [5]).

III.

ARCHITECTURE IMPLEMENTATION

The procedures of the proposed ADL analysis architecture cover from the data acquisition phase to the data analysis phase. An overall flow chart diagram is illustrated in Fig. 1. The former phase includes a module for MapReduce-assisted [19] activity recognition model training. The latter phase is for request-oriented ADLmining service delivering. This section will therefore be divided into two separate discussions, dedicated to the two phases.

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Figure 1. ADL analysis architecture flow chart

another. An illustration is presented in Fig. 2, where variable Ait is defined by the state of the ith activity and Ojt is defined by the state of the jth observation, extracted feature out of sensor values. Ait equals one generally meaning that the observed resident is taking activity i in the time slot t, and zero if otherwise. We also define an “on” state feature by equaling Ojt to one, and zero for “off”. In detail, the activity recognition model MAR to be trained is composed of two kinds of parameters. The transition probability P(At|At-1) and the observation probability P(Ot|At). By using maximum likelihood estimation (MLE), we can derive the closed-form formula for the model retraining as the following equations, where 1 represents the indicator function: 1 ( At = a, At −1 = a′ ) t P ( At = a | At −1 = a′ ) = (1) 1 ( At −1 = a′ )

A. Data Acquisition Phase To enable a successful data collection, an accurate activity recognition mechanism is indispensible. According to our previous work [12, 20], we have adopted an ambient wireless sensor network (WSN) to naturally collect data for activity recognition inference which consists of a simplified two-tier Bayesian network (2TBN) model. The model is a hybrid dynamic Bayesian network (DBN) model with Naïve Bayes classifier. The model, after trained by sufficient labeled sensor data, is used by the activity recognition engine for further automatic activity classification. The main issue of the mechanism lies in that human behavior can dynamically change over time. For example, if the pressure sensor on the sofa perceives a surging sensing signal (effectively indicating a sitting action of a resident), this means that the resident could be reading newspaper (on weekdays) or watching TV (on weekends). To deal with such dynamically changing nature of contexts, a naïve solution is to retrain the activity model on a regular basis. However, this requires us to store all sensing data along with their corresponding labels, which can be annotated by either the prediction of the inference engine or by user manual annotation if the prediction is wrong). Even worse, the retrained model is vulnerable to outlier data if the amount of training data used for retaining is not sufficient. Furthermore, a retraining process with a largebatch of training data can be overloaded for a regular household appliance. It is therefore unaffordable for a regular home setting to effectively deal with such dynamic context changing. In this work, we used a unified cloud platform to maintain sensing history along with the activity labeling. In addition, the platform is also responsible for the model retraining of all participants. The latest retrained model is updated by the activity recognition engines deployed at home in order to response to the latest context changes.

¦

P ( Ot = o | At = a ) =

¦

t

¦ 1 ( O = o, A = a ) ¦ 1( A = a) t

t

t

t

(2)

t

In order to do the parameter training in the cloud environment, we choose the prominent EM algorithm [21], tailored to the MapReduce programming model for leveraging computation scalability, which is the main constraint of most non-cloud training solutions. As EM algorithm is known to be composed of two iterative steps, called Expectation step and Maximization step. In maximization step we try to evaluate the parameters maximizing the data likelihood by using the equations mentioned earlier. Due to the nature of associativity and commutability in these two equations, it is suitable to use separated MapReduce program instances to compute the four factors in the equations (two in the denominators and two in the numerators) in parallel. In expectation step, we compute the most probable labels (i.e. variable A) with the parameters MAR computed in the previous maximization step. If we here neglect the effect of state transitions, that is, the effect of transiting from At-1 to At, we can again use MapReduce program instances to compute labels to all time slots independently in parallel. This attempt reduces the overall prediction accuracy by acceptable amount in trade of higher calculation speed. The two steps repeat themselves until the parameter MAR is converged according to its predefined threshold. A simplified pseudo code snippet for the MapReduceassisted EM (MREM) algorithm for 2TBN model training is given below.

Figure 2. 2TBN model for activity recognition

1 2 3 4

We also mention that the 2TBN model used here is composed of multiple mutually independent chains. That is, we assume that the activities are independent with one

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MREM(A1:T, O1:T) For all possible Ai do For a = 0 or 1 do Enumerate all t such that Ait = a For a’= 0 or 1 do

5 6 7 8 9 10 11 12 13 14 15

Enumerate all t such that Ait=a, Ait-1 = a’ Calculate P(Ait=a|Ait-1=a’) For all possible Oj do For all combination of o, a do Enumerate all t such that Ojt=o, Ait=a Calculate P(Ojt=o|Ait=a) If MARуMAR(old) then terminate Else Upload MAR Use MAR to update most probable A1:T Go to 1

architecture is illustrated in Fig. 4. The main objective of the service is to serve as a cloud platform where ADL related cloud applications can be readily accessed and built. Working like a black box, the ADL analysis platform receives requests from its service users (possibly 3rd party cloud service providers) in the format of an XML-embedded-document and sends an analysis result (also an XML-document response) to users after some guaranteed time period (provided the request specification valid) and actively pull it back from the platform. The result can be later used for fulfilling its own cloud services, usually healthcare related.

To sum up, for fulfilling the data acquisition of the proposed architecture of the ADL analysis service, we have a deployed ambient WSN and an activity recognition engine for ADL prediction and labeling at the home side, and a cloud platform with the MREM algorithm as its core for model retraining at cloud side. The activity recognition engine is responsible for activity labeling and delegating data upload to the home gateway, including both sensing data (variable O) and its labeling (variable A).While the MREM instance is responsible for routinely retraining the parameters of the latest responsive activity recognition model. An operating data acquisition part of the architecture is illustrated in Fig. 3.

Figure 4. Block diagram of data analysis phase

For delivering data analysis service, our architecture incorporates the following components: a request dispatcher which serves as the main gateway responsible for service interface, which also helps extract detailed requests and practice load balancing by dispatching the requests to the computing resource that requires the least waiting time for completing the task. A pre-clustering module which provides customizable finer-grained analyzing results and the central mining module that does the real data analyzing. In implementation, we propose a RESTful [22] fashion interface for higher service accessibility. Service user can ask for any available resource by using corresponding URL. The analysis service delivered can be further categorized into three categories: Simple ADL property mining aims for 1. delivering preliminary data mining over a single ADL property. The user needs not providing request specification for this type of queries and a GET HTTP message is used. Sophisticate ADL property mining require a 2. service user to supply a request specification in an XML-document, thus using a POST HTTP message. This type of query does complex mining such as analyzing over combinatorial properties induced by multiple ADL properties, e.g. mining over a weighted sum of ADL properties representing a rough health index. Our prototyped architecture also handles queries for gradation (mapping original values

Figure 3. Block diagram of data acquisition phase

Here we assume that we can guarantee the accuracy of our activity recognition engine (user feedback can also compensate for the tolerable inaccuracy) so that we can assume that the labels resided in the cloud database is reflecting the true behaviors of participants. These activity data hereafter will be altogether called ADL data, and an ADL property will be used for denoting a single activity data. Basically all properties induced from those ADL data are numeric value using some simple inspection. For example, we can infer the total sleep time (an ADL property) by summing up the proceeding time period of the sleeping activity (the ADL data from activity recognition result). B. Data Analysis Phase Having ADL data prepared, The system is ready for analyzing task. Another yet the most essential part of the architecture will be dedicated to delivering an accessible ADL data analysis service. An overview of the

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counterparts. Actually, the Mahout project mentioned earlier also depends on MapReduce programming framework. The domain-specific parameters needed during the computation can be found in the interpreted request specification from the request dispatcher module. The analysis results along with processing logs are stored on a private cloud file system for later user retrieval. Fig. 5 illustrates the whole process flow in a sequence diagram conclusively.

to discretized level for different abstraction view of data). Sophisticate ADL property mining with pre3. clustering in many ways resembles ordinary sophisticated query, but giving a finer-grained analyzing result because the population is divided into groups according to customizable clustering specification. The clustering process can refer one or several inherent properties, e.g. age, sex, or average income. The pre-clustering specification is also specified after the request of a specification document. After the request is received by the dispatcher module, the module automatically speculates the status over CPU usage of VMs; it then dispatches the interpreted request specification (if there is one) to the least loaded cluster for fulfilling the guaranteed maximum response delay. Reversely, users must follow some obligation such as not sending intensive requests at a rate exceeding some regulated maximum request rate. After dispatching, the module sends a handler back to the service user along with an authentication code for later result retrieval. Upon receiving a task, the cluster dedicated for the ADL analysis will first do preclustering using the k-means clustering algorithm if specified to do so. In the current prototype, we choose the Apache Mahout project, an open source project focusing on constructing data mining and machine learning methods, in the cloud environment.

IV.

APPLICATION EMBODIMENT

In this section, as an example we present a possible healthcare service build upon the proposed ADL analysis architecture. This however shall not restrict the applicable scope of the architecture to other services. All ADL-related services such as commercial services can also benefit from the ADL-analysis service. The discussed healthcare service aims for promoting health quality for communities concerning their health improvement. For establishing an index for “health quality,” we utilize the weighted sum of levels of ADL properties according to domain knowledge, by a gradation specification of the sophisticate mining request. This index serves as a rough but intuitive index for health quality. The possible application creator based on the index can be a medical foundation or a caregiving center, and the end user can be an elderly or the disabled, along with their families and caregivers. In order to raise the health quality of a community of interest, we promote the health quality of each individual by applying persuasive technology. The central idea is to suggest each individual in the community to perform a healthier daily living style if one is living in an unhealthier living style comparing to the whole community. Here the community refers to the set containing all participants in our cloud-tier database. To start with, the service application (the user of the ADL analysis service) tries to investigate if an end user, e.g. Edward, enjoys a living of high health quality or not. Here we assume that the service provider is capable of practicing ADL data requisition (such as the WSN-based activity recognition mentioned previously in this work), and it has its way of evaluating health quality. All the service providers need in order to complete the service is to get the health quality of the community to compare with the one from each individual. The health quality promotion application hence asks for the ADL analysis service for help. It essentially requires the following service in detail: asking for the median of the index of the health quality among all participants in the community (hence a sophisticated ADL property mining requests for the combinatorial term “health quality”). For increasing the service reasonability, the application further requires the pre-clustering request on age, sex, and marriage condition as referenced properties, for a

Figure 5. Sequence diagram of data analysis phase

To proceed, the task is transferred to the central mining module. The design of the central mining module aims to cater for the analysis needs. Since many data mining algorithms can be divided into summation subtasks, we can easily find their MapReduce algorithm

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finer-grained. It can then use the result as the comparison criteria for the end user Edward. That is, his health quality is compared with the median of the health quality of the group that is the most closest to his background and given encouragement over a healthier life style by an intelligent interactive interface in advance if was found poorer than the community median. According to the protocol defined by the analysis service, the application can send a POST HTTP message to the public service interface and this message will be received by the request dispatcher. The snippet of the request specification message in the form of an XML document is shown below. The specification will trigger both the pre-clustering process (which references two inherent properties, Age and Sex) and a gradation process (using criteria indicating in the bound tag). Also, as specified, the required combinatorial property named HQ is a linear weighted sum of two ADL properties, Sleeping and Eating.

its center information and the result of the desired combinatorial property. 71 Male 68 58 Female 72 32 Male 55

(Sent to URL http://domain/mining/api/median/HQ) Age Sex LinearFormula Sleeping 4.0 180:600 240:540 300:480 360:420 Eating 3.0 1:7 2:6 3:5 3.5:4.5

A possible application scenario is depicted below. Knowing the similarity between Edward and a group, e.g. here group three (by comparing Edward’s pattern with those of all groups using a predefined distant measurements), the application then compares the health quality of Edward’s and that of group three. It finds that the health quality of Edward’s is poorer than that of group three. For giving more detailed advisory on the living style, the application then sends a simple ADL property mining request to the service interface for obtaining finer-grained comparison with ADL properties which may cause the deterioration of Edward’s health quality. The application can still use similar service protocol for completing the whole service and giving effective advices for improving one’s overall health quality. The detailed operation is illustrated in the sequence diagram in Fig. 6 below.

Upon receiving the service handle and authentication codes, the application appends the handle along with the codes as a message header after a predefined waiting period in order to retrieve the analysis result. A possible response may look like the XML document snippet below. The analysis result is divided into three independent groups relating to three clusters, each with

Figure 6. Sequence diagram of health quality promotion service

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V.

Service Completion Time(s)

Other than interacting directly with the end user such as Edward in the example, we can also incorporate the health quality promotion application using mobile devices to timely inform his families or caregiver. The history of his health quality can also be maintained somewhere else in a 3rd party cloud platform for further medical inspection. The best of the cooperation between the application and the ADL analysis service architecture is that the dynamic change of ADL data (due to human’s behavior changes) in the cloud data warehouse can be always captured as time shifts, which is an advantage over most of the prevalent persuasive technologies and services. One thing worth noticing is that there is no strict division between a 3rd party cloud service provider and the maintainer of the ADL analysis platform, since they can also be a unified architecture entity that has the analysis service open in both public and private (for the health quality promotion application) usage simultaneously.

380 360 340 320 300 280 260 240 220 200 4

8

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16

Number of cluster nodes Figure 7. Broken line chart for Service Completion Time using different cluster size

It can be shown that the completion delay of a service converges to about 3 minutes 46 seconds when the number of processing nodes increases, for an analysis of 100K population size. A complete computation involves about eight individual MapReduce tasks, six of which are initiated for clustering and the remaining two are for data preprocessing and analyzing. For a cluster of 16 nodes, a MapReduce task completes at about 24 to 30 seconds in average, which is the average time required for completing a simple mining request. For a larger scale analysis, say 500K and 1000K population size, the total computation time raises to 4 minutes 44 seconds and five minutes seven seconds respectively, using a cluster of 16 nodes. It is likely that the delay can also be shortened by scaling up the cluster to adequate number of computing nodes. As an analysis platform, the current prototype seems not satisfying the criteria for an online analysis service. The bottleneck can be the huge startup overhead of MapReduce. For dealing with this issue, some works propose to increase the overall throughput and to shorten the individual startup delay [24], and we can also try to increase the number of computing groups which serve as alternative computing resources that can be assigned tasks by the request dispatcher. In addition, it can be helpful if caches can be used for answering popular or repetitive ADL analysis. An optimistic expectation service completion time can be lowered to about 90 seconds or less. It is also the most urgent for our future work to complete for improve the overall quality of service delivery. Using Amazon EC2/S3 [25, 26] as a possible candidate for building such an ADL analysis service on a scale of 1,000K population size, an ADL analysis service provider is cost only $192 to complete 10,000 continuous sophisticated mining requests with preclustering. The number is composed of $72 for about 850 operating instance hours ($0.095 per hour), $50 for ADL

EVALUATION

In this section a prototype of the proposed architecture including the data acquisition phase and data analysis phase are evaluated by their average service delay. The network delay between the platform prototype and the service user will be neglected here, for that it will not be the main concern. Additionally, because of the tolerance of real-time demand of the activity recognition model retraining (using MREM algorithm), we also neglect the evaluation on data acquisition architecture but focusing more on the data analysis service quality. In terms of the configuration of the testing environment, we use 16 homogeneous VM nodes rented from Roystonea [23], a virtualized cloud infrastructure hypervisor service developed by National Taiwan University. Each VM is equipped with the framework of Apache Hadoop (v0.20), including HDFS, MapReduce and HBase. All nodes have 2GB memory along with dual-core CPUs. For leveraging the potential locality speedup of MapReduce, the ADL data is partitioned based on the number of cluster nodes so that computing tasks can be assigned to the nearest node as possible. In order to evaluate the performance of large-scale ADL data analysis, the experiment is held using the simulated normally distributed data over seven ADL properties collected from a large population of virtual participants, according to investigated daily live patterns of ordinary elderly. Our bench mark will be the averaged total computation time required for completing a service request, which empirically simulates the service completion delay of the proposed architecture. An evaluation result over a sophisticated mining request for median with pre-clustering on a population of 100K participants is listed below. The parameter for k-means clustering is three for k with a threshold of 0.001.

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[7]

data uploading and request message transfer, and about $70 for ADL data maintenance. VI.

CONCLUSION

[8]

It is generally acknowledged that cloud computing helps much in pervasive technologies such as healthcare related applications. In this paper we have proposed using cloud environment as a platform for implementing a readily accessible large-scale ADL data analysis architecture on which ADL-related healthcare applications can easily be built. The architecture includes the ADL data acquisition phase, which uses a hybrid 2TBN model along with tailored MREM algorithmenabled retraining module, and the data analysis phase, which serves three different types of ADL analysis queries. Though not quite efficient in service completion time speculation so as to serve highly-active healthcare application in real-time for current prototype, the architecture is proved cost-effective and can be potentially scaled up for improving the performance and the quality of the analysis service. As indicated by the embodiment in section IV, the architecture is applicable in many ADL-related fields, especially the health-care area, for it directly helps perceiving harmful long-term living habits or patterns. The analysis service can probably give its hands to those commercial applications. Our research team so far aims for improving the overall speed on processing queries, thus satisfying those query-intensive applications. As part of the future work, we will also try to find more possible combination of useful applications that incorporate with the architecture proposed in this work.

[9]

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[13] [14]

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[17]

[18]

VII. ACKNOWLEDGMENT This research is sponsored by National Science Council, R.O.C. under the grant number NSC 99-2218-E-002-002, and Ministry of Education, R.O.C. under the MoE ATU plan.

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