a resilient distributed framework

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Context Aware Sensing – What’s the Significance? Surapa Thiemjarus, Benny P. L. Lo and Guang-Zhong Yang Department of Computing, Imperial College London, UK

ABSTRACT – One of the most promising applications of Body Sensor Networks (BSN) is long-term ambulatory monitoring. This paper discusses the key technical challenges associated with context aware sensing for these environments. It outlines the importance of relating physiological activity with the sensed signal for accurate episode detection and trend analysis. KeyWords – context aware sensing, activity recognition, body sensor network INTRODUCTION Context is defined as “the circumstances in which an event occurs” [1]. This concept has been successfully employed in information processing for over 50 years, particularly in Natural Language Processing (NLP) and Human Computer Interaction (HCI). The method is becoming increasingly popular in pervasive computing research in recent years due to the diversity of the environment under which the sensed signal can be collected. To enhance the practical value of a pervasive sensing system, the contextual information can be utilised as an additional input for the adaptation of computing devices or services so that they can provide a more intelligent support to their users. According to the model proposed by Albrecht et al [2], context can be classified in a broad sense as context related to human factors and context related to the physical environment. The context related to human factors can be subdivided into information about the user, the user’s social environment and the user’s task. Similarly, context related to physical environment can be subdivided into location, physical conditions and the system infrastructure. Context awareness for BSNs is mainly concerned with the interpretation of physical activity or physiological condition under which the biological signals are collected from on-body or implanted sensors. However, additional information about user’s interaction with surrounding environment can also be obtained from ambient environment sensors [3,4]. By observing the correlation between the physiological status and environmental factors, reasoning about the underlying causes of adverse events and their effects can be achieved. For example, Someren et al [5] illustrated the potential use of motion signal in analysing the effect of medication intake by calculating the average medication responses during different times of the day for Parkinson patients. These profiles can be used in evaluating the pharmacological interventions.

Bhattacharya et al [6] investigated the heart rate, oxygen uptake and acceleration profiles during exercise to study the relationship between body movement/acceleration and metabolic rates. In addition, the context information can also be used to assist the diagnosis based on the acquired physiological signals. This is because similar sensory signals can be interpreted differently depending on the activities that the patients are engaged in. For instance, the underlying cause of a rapid heartbeat and degenerated ECG signal can be due to vigorous movements of the patient during exercising rather than a cardiac episode. In addition, the motion signal can be used to recover bio-sensor signals corrupted by motion artefacts. An example of such work is the noise cancellation algorithm proposed by Asada et al [7]. In their study, the algorithm has been applied to recover corrupted Photoplethysmography (PPG) signal based on the signal obtained from a MEMS accelerometer. In general, a record of patient daily activities can provide an indication of the general well-being. For patients with disabilities, monitoring the tasks that require more effort to accomplish can be used as an objective measure of their functional ability [8,9]. Automatic activity recognition is an active area of study in pervasive computing and most of which relies on analysing the characteristics of motion sensors [10,24,28,29,30,32,33]. Other sources of information, however, can be used for activity recognition. For example, by taking the privacy and usability into account, Chen et al [11] proposed an automated bathroom activity monitoring system based on the acoustics information. A bathroom activity monitoring system can potentially be used to enhance the understanding of personal hygiene behaviour in dementia patients. Other motion characteristics such as changes in posture and gait have been demonstrated as a useful predictor of certain illness and neurological abnormalities [12,13,14,15,16]. Walker et al [17] illustrated a simple use of a continuous patient activity monitoring system for exploring the relationship of activity and disability in patients with rheumatoid arthritis (RA). Affective states of depression, anxiety and chronic anger have also been shown to impede the immune system and physiological pattern recognition of affective states can potentially be used to assess stress, anger and other emotions that can influence health.

Teicher [18] studied the correlations between different activity levels and psychiatric disorders. Myrtek and Brügner [19] investigated the perception of emotional events in everyday life by assessing the correlations between physiological parameters, such as heart rate and physical activity, and psychological parameters. Picard et al [20] described a framework for emotion recognition and demonstrated its use for detecting anger, hate, grief, platonic love, romantic love, joy and reverence based on signals from EMG, blood volume pressure, skin conductance and Hall effect respiration sensors.

provides more detailed objective measures of body functions.

In areas other than health diagnosis, the user’s context itself is potentially useful information. Bardram [21] illustrated the concept of context-awareness in hospitals. RFID tags were used to identify the patient lying in the bed, as well as clinicians and surrounding medical equipment. A context-aware pill container can recognise patient from the finger-print and allow a proper dose to be administered. Tognetti et al [22] demonstrated the use of limb gesture detection for post-stroke rehabilitation. The use of a wearable system for clinical management of individuals undergoing rehabilitation is very attractive since it allows the recording of quantitative measurements in settings other than hospitals or clinics. The development in gesture recognition allows the use of gesture for user interface. An example of such work is the integrated sensor dance shoe developed at MIT [23]. The shoe detects feet motion and uses this information to control music. In many studies, the use of context information for indoor navigation has been explored. Lee and Mase [24] used two sets of wearable motion sensors to recognise walking activities. They applied a dead reckoning technique to estimate the current location of the user. Golding and Lesh [25] explored the potential of using wearable sensors such as accelerometers, magnetometers, temperature and light sensors for detecting a user’s location. Wilsen and Christopher [26] developed a deterministic finite state machine, called the narrator system that generates a concise, readable summary of user location, duration of time spent in a location and simple activity based on measurements from motion detectors, pressure mats, drawer and door switches and RFID systems.

Optimal Sensor Placement: The difference in power consumption of a BSN node during radio on/off is illustrated in Fig. 1. Similar results were shown in mica motes [27]. From the diagram, it is obvious that power can be greatly saved simply by not transmitting the nonuseful data. Identifying sensors that have direct implication to the decision process is advantageous in that it can be used not only offline to determine optimal sensor locations, but also online to dynamically enable/disable the sensors depending on the current/predicted context. In [28], we have proposed a feature selection based approach as a solution to this problem. Feature selection is a dimensionality reduction technique that allows elimination of (irrelevant/ redundant) features. In the context of BSNs, it implies less data transmission and efficient data mining. It also brings potential communication savings in term of packet collisions, data rate and storage. The algorithm we use is a filter-based method called Bayesian Framework for Feature Selection (BFFS). The virtue of the method is that the selection of features is purely based on the data distribution and thus is unbiased towards a specific model.

SENSOR FUSION IN BSN Data fusion in BSNs is a challenging task and it is difficult to resolve all the technical issues by using a single technique. In the study of BSN at Imperial, we have developed complementary data modelling techniques for multi-sensor data fusion with BSNs for activity recognition. Our approach to multi-sensory data fusion in BSNs is based on the following concepts:

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Traditionally, the knowledge of the context of the user is acquired by means of self-reporting based on diaries or questionnaires. This method is both time-consuming and unreliable, especially for elderly or subjects with memory impairment. Another method of acquiring context information is through clinical observation. This method usually relies on specialised equipment and a dedicated laboratory set-up. In addition, measurements made in the clinic may not accurately reflect patients’ behaviour in their normal environment. With the current advances in sensors and wireless technology, the concept of BSN has been developed to provide an unsupervised ambulatory monitoring under users’ natural physiological status. It is less invasive and

Fig. 1. Difference in power consumption of the BSN node with/without turning on the transmission module. Noise Detection and Filtering: Data obtained from the sensor network can be misrepresentations of real world due to faulty sensors (noisy or node failure), unreliable network communication and motion artefacts. To overcome the problem of noisy/missing data and increase system reliability, some redundancy must be introduced. In [29,30], we have proposed the use of a Bayesian network (BN) with hidden nodes for data fusion in a multi-hop network environment. By using the hidden nodes for resolving data dependency, the

model robustness towards noise can be improved. The data dependency also provides a means of detecting noise within each logical subnet of redundant sensors. BN relies on a message passing inferencing algorithm which is potentially useful for distributed sensing systems. This strength was demonstrated by Paskin et al [31] on sensor calibration within a sensor network.

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based adaptation. The first category involves modification of model parameters, while the latter can remove the undesired variability simply by transforming the observation features. A technique for the adaptation of model parameters in BN has been shown in our previous work [35]. Krause et al [33] and Kristof [36] have illustrated techniques for online learning in a SOM. The model families we selected, therefore, are likely to be capable of handling with variations from irrelevant sources. The feature based adaptation is usually in some forms of data normalisation. A good example is for handling day-to-day variations as proposed by Picard et al [20].

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Fig. 2. A schematic diagram of a noise resilient distributed Bayesian framework, where ‘d’, ‘h’, and ‘s’ indicate the decision node, intermediate hidden node and observed sensor nodes, respectively. Higher-level Temporal Constraints: Human activity involves body movements that are continuous in nature. It has also been illustrated in [29] the model accuracy can be significantly improved when the smoothness constraint is enforced. This can be achieved simply by averaging the instantaneous model beliefs over a fixed size temporal window. A more complex model can be used in the ‘supervising layer’ to enhance the recognition accuracy. For example, a first order Markov model can be used to encode the information about context transitions [32] or transitions between unsupervised clusters [33]. On-Board Processing: In addition to the battery issue, continuous transmission of raw signals can also lead to an excessive amount of data. Direct transmission of raw signals to a central processing unit is therefore inappropriate. To prolong the lifetime of sensor nodes, on-board processing is required. To this end, a hierarchical Self-Organising Map (SOM)-based architecture for spatiotemporal signal processing has been proposed. A SOM is a topological array of neurons which can be realised as an analogue on-chip circuit [34]. The key concept of the Spatio-Temporal SOM (STSOM) architecture is the use of signal characteristics to enhance model accuracy and compactness. DISCUSSION Context aware sensing is an important topic in pervasive sensing. Apart from noise, the system should be adapted to different sources of variations such as inter- and intra- subject variability. The adaptation for handling different sources of variations can be divided into two main categories: model-based adaptation and feature-

Another issue is concerned with filtering out the irrelevant context. While in most supervised models, unknown classes are assumed to share the same model distribution. SOM is an unsupervised learning technique and no class information is needed during training. This allows several unlabelled clusters to be formed for unknown classes. A potential method for handling unknown classes in SOM is to assign the class label to those neurons with high number of supports or neurons within a close distance to the neurons with high number of supports. Context can be classified as different levels and can be linked to different levels of representation. In motion analysis, for example, Bobick [37], divided the machine perception of motion into three levels, namely movement, activity and action. Similar to the concept used in speech recognition, we may need to break the movement down into some well-defined morphemes to achieve a high recognition accuracy. Ontology on how an activity is segmented into morphemes can be used in the upper layer to enhance the accuracy, similar to the case of language models in speech recognition. CONCLUSIONS In this paper, we have discussed the significance and our practical experience of using context-aware sensing for BSN. The distributed approach we have proposed consists of optimal sensor placement with feature selection combined with a noise resilient distributed Bayesian framework. We also discussed the use of high level constraints to enhance recognition accuracy and the potential strength of the STSOM architecture for onnode processing. The techniques we proposed have been validated with experiments on activity recognition and the results have shown their potential value for practical deployment of BSN. REFERENCES 1 http://www.dictionary.com 2 A. Schmidt, M. Beigl and H.W. Gellersen, “There is more to context than location”, Computers and Graphics, vol. 23, no. 6, pp. 893-902, 1999. 3 E.M. Tapia, S.S. Intille and K. Larson, “Activity recognition in the home setting using simple and

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