Enabling affordable and efficiently deployed location ...

Technology and Health Care 17 (2009) 221–235 DOI 10.3233/THC-2009-0549 IOS Press

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Enabling affordable and efficiently deployed location based smart home systems Damian Kellya,∗, Sean McLoonea and Terry Dishonghb a Electronic

b Digital

Engineering Department, National University of Ireland, Maynooth, Ireland Health Group, Intel Ireland, Leixlip, Kildare, Ireland

Received 6 February 2009 Abstract. With the obvious eldercare capabilities of smart environments it is a question of “when”, rather than “if”, these technologies will be routinely integrated into the design of future houses. In the meantime, health monitoring applications must be integrated into already complete home environments. However, there is significant effort involved in installing the hardware necessary to monitor the movements of an elder throughout an environment. Our work seeks to address the high infrastructure requirements of traditional location-based smart home systems by developing an extremely low infrastructure localisation technique. A study of the most efficient method of obtaining calibration data for an environment is conducted and different mobile devices are compared for localisation accuracy and cost trade-off. It is believed that these developments will contribute towards more efficiently deployed location-based smart home systems.

1. Introduction The predominant benefit of smart homes is that they make the activities of everyday life more convenient for their inhabitants. Recently the importance of smart homes has been heightened by the fact that they can be used to actively provide health care services to an elder. The availability of health care services to an elder in their own home means that elders who would traditionally require attention from carers can have much of their supervision needs provided for by their smart home. This omnipresent monitoring facility is envisaged to allow elders to live in their own home for longer before requiring a more specialised care environment. A wide variety of services can be provided to an elder by their smart home, including; monitoring of activity patterns [17,20], provision of activities to keep the elder proactive [15], detection of safety critical conditions like falls [5] and medication adherence promotion [13]. As with most smart home functionalities, these technologies require some technique to detect the current context or activities of the user. To infer the context of the user a number of sensors are typically employed. These sensors can be anything from simple contact switches on furniture to RFID proximity sensors. Our work focuses on the location sensing component of smart home systems. As will be presented in Section 2, there exists a wide variety of smart home functionalities which depend largely on the location of the inhabitant as an input. Many of these location-based smart home systems require an array of sensors to be installed throughout the home environment, typically at a level of one sensor per room. ∗

Corresponding author. E-mail: [email protected].

0928-7329/09/$17.00  2009 – IOS Press and the authors. All rights reserved

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Hence, the location sensing element of these systems have high installation overhead, a trait which overshadows the obvious benefit of such systems. We are working towards implementing a location sensing technique with minimal hardware requirements which can reduce the installation overhead of location-based smart home systems. The location sensing technique under development utilises the signals from the subject’s mobile phone, arriving at a single Bluetooth enabled basestation computer. The restriction of a single basestation prevents the use of signal strength triangulation or proximity localisation techniques. Instead a new technique must be utilised which incorporates all of the signals available from the single Bluetooth Access Point (AP) into the location predictions. Section 2 outlines work which has similar elements to ours and indicates our contribution towards more affordable location-based smart home systems. Section 3 continues by describing the components necessary for our location tracking framework. Next, Section 4 describes the methods by which data is obtained from the proposed hardware and illustrates the behaviour of the available signals in a home environment. The classification algorithms applied to this data to resolve the subject’s position are then described in Section 5. Sections 6 and 7 present results on the tracking accuracies achieved with each algorithm under a range of conditions and the conclusions that can be drawn from these results. Finally Section 8 highlights the additions to our work necessary to lead to the development of a more complete localisation system. 2. Related work To highlight the relevance of our work, this section presents some smart home systems which utilise location information to aid in their provision of services to an elderly inhabitant. Following that a brief survey of the location tracking technologies which could and have been used in a home environment will be conducted and the decision of the most suitable technology for our work will be discussed. 2.1. Smart home environments To date a variety of elder care smart home systems have been proposed, many of which utilse location as the main form of context. One of the most obvious uses which can be made of location information is to allow monitoring of an elder’s activity patterns over long periods of time. One commercially available elder monitoring system is QuietCare [17]. The QuietCare system uses Passive Infra-Red (PIR) motion detectors in each room to infer the current location of the elder. Then deviations of the elder’s movement and activity patterns from typical healthy patterns can be detected and a caregiver can be informed. A context sensitive medication prompting system is presented in [13] that infers the subject’s context from their room-level location, also based on PIR sensors. Based on the subject’s location, different prompting devices throughout the environment are used to remind the subject to take medication. A portable wristwatch-like prompting device is used when the elder is in a location where no other form of prompting device is available. Furthermore medication prompts are sent only at times when the elder’s motion patterns indicate that they would otherwise miss a dose. For example if the elder exhibits a motion pattern which, based on baseline data, indicates they may leave the house at a time close to their usual medication time, they are prompted to take their medication before they leave the house. This reduces occurrences of missed doses. Another piece of work, which uses PIR sensors to infer location, but with resolution finer than room level is presented in [4]. It is achieved by placing several PIR sensors in a each room, one sensor for

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each location of relevance within the room. That particular work is focused on assessing the subject’s levels of mobility, which is indicative of motor behavioural disorders. Statistics of a patients motion patterns over a typical 24 hours are visible from the trial data. However, it is indicated that PIR based location predictions are not reliable when a caregiver enters the environment due to PIR sensors inability to differentiate between different people. A system which uses an ultrasound location tracking technique is presented in [9]. With their highaccuracy tracking technique, the authors developed a remote monitoring application, similar to that provided by the QuietCare system. This application provides location markers on an environment map, in real-time, to interested parties with the necessary software. The authors also present an attention capture application which provides interactive displays to an elder to gauge their reactivity to certain types of prompts. The location information is integrated into the decision of which environmental display to use to engage the inhabitant, which is similar to the approach taken by Lundell et al. in [13]. Finally the authors propose an indoor navigation system to assist visually impaired subjects. Navigation requires a high accuracy tracking technique such as ultrasound to allow useful directions. As such, the tracking system which we will later present is not suitable for precise indoor navigation. One further use of indoor location is outlined by Chen et al. in [5]. This paper describes a sensor for detecting falls of an elder. When a fall is detected it is necessary to be able to pinpoint the location in which the fall occurred to allow emergency personnel to quickly locate and assist the individual. Not much information is given about the localisation technique, except it uses the portable fall sensor’s transmission radio signal strength at several basestations to triangulate the subject’s location. As this section highlights, there are many smart home systems which utilise location information. Localisation techniques of varying resolution are employed in different situations. However in a home environment room-level location is typically sufficient, which explains the ubiquity of PIR localisation techniques. 2.2. Location sensing technologies Location sensing smart home components, however varied, typically share one trait; many devices must be deployed throughout the environment to obtain the desired levels of accuracy. To obtain roomlevel location with PIR sensors at least one device must be installed per room, and even this technique fails when there is more than one person in the environment. Visual tracking is a technique which is capable of high resolution localisation but at the expense of sophisticated and costly equipment, see [12] for example. In the case when less sophisticated camera hardware is available, one camera can be deployed per room and face recognition algorithms can be employed (e.g. [23]). This would permit location accuracy levels comparable to PIR sensors, with the added ability to discriminate between different subjects. The presence of cameras in a home environment is generally considered intrusive and introduces privacy concerns for an elder. To overcome the privacy concerns associated with visual tracking, two types of technologies can be employed, Infra-Red (IR) and Radio-Frequency Identification (RFID). Infrared technologies similar to that presented in [21] can easily be adapted to allow room-level location predictions to be generated. IR tracking works when the subject to be tracked carries around an IR emitting beacon and IR receivers in each room resolve the location of the subject from the unique IR code. IR tracking is well suited to indoor room-level localisation since the walls act as a natural container for IR waves. RFID tracking techniques are similar in that each room requires a RFID reader and a subject carries around an RFID tag (e.g. [6]). The advantage of this method over IR is that the tags carried

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by the subject do not need to be powered and are relatively cheap to manufacture. The disadvantage of RFID tracking is that RFID signals are not blocked by walls and mechanisms must be introduced to account for the detection of tags in several different rooms at once. The localisation technologies listed thus far can reliably determine room-level location, at the expense of requiring a large amount of hardware to be deployed throughout the environment. Ultrasound technology permits the estimation of the distance between a subject and the installed receivers by monitoring the time of flight of the ultrasound signal from the subject. From these distance estimates, triangulation can be used to resolve the subject’s exact location. According to [9] location predictions within 3 cm of the true position are possible with only 4 receivers installed in the trial home environment. Aside from the high cost of these devices, real home environment walls would not provide such a favourable propagation environment, which is why the manufacturer recommends 2–3 receivers per room in a real environment. Where such accuracy is not economically justifiable, Radio Frequency (RF) technologies provide a more cost-efficient solution. RF signals are far more capable than ultrasound at penetrating wall obstructions, hence less detectors are necessary throughout an environment than any other technologies. However the increased transmission speed of RF signals over ultrasound means that hardware which supports time of flight estimation is not commonly available or is very expensive. Instead a Received Signal Strength Indicator (RSSI) reading can be used to estimate distances for triangulation. However, RSSI is not as accurate an indicator of distance as time of flight due to attenuation from obstructions. To account for the non-linear variation of RSSI throughout an environment, fingerprinting techniques are generally utilised instead of triangulation. Fingerprinting involves using a set of training data indicating the unique tuple, or fingerprint, of RSSI values from each access point for every possible user position. Then in the prediction phase new samples are compared with the training data using a variety of techniques to determine location. Examples of RF communication technologies which have been used for localisation include WLAN [19], Bluetooth [3], Zigbee [18] and cellular networks [16]. The installation of several RF access points permits triangulation or fingerprinting techniques to be employed. However, we propose that the installation of several access points also presents a less than ideal deployment situation. Our work is concerned with ensuring the minimal amount of hardware is installed in the environment. To this end, the only installed access point is that of the location tracking computer or basestation computer, as will be described in the next section. 3. System design Based on the survey of localisation systems in the previous section it was decided that RF-based localisation is the most suitable for efficient home deployment. RF localisation is a large field of research in itself with many different solutions. Hence, this section outlines the selection of the exact RF technology on which to base our low-infrastructure localisation technique. 3.1. Localisation platform WLAN location tracking is a mature area of research, which has exhibited many compelling results [19, 22]. However, most WLAN location tracking techniques are validated in office environments with multiple WLAN APs detectable throughout. As a result, these techniques are not as applicable to a home environment. The availability of WLAN devices in an elder’s home, particularly in rural areas, is generally quite low. Even if a WLAN AP is available, multiple APs would be necessary to provide

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Fig. 1. Bluetooth access point, constructed using a Blueradios BR-SC30N Bluetooth module.

sufficiently accurate location predictions, with location prediction accuracy increasing with the number of APs. Installing multiple WLAN APs throughout a home environment is an option if there are no cost constraints. Along with the expense of deploying several WLAN APs there is also the necessity of deploying a wired network throughout the environment to relay information back to the basestation computer. Our work focuses on single AP localisation to eliminate the installation and cost overheads associated with multiple AP localisation. As well as the installation restrictions precluding the use of a multiple WLAN AP localisation system, there also exists power consumption limitations. A mobile device to be carried by a subject must be able to last at least a day before the battery needs to be recharged. When active, WLAN devices consume large amounts of power, especially when compared to an energy efficient wire-replacement technology, such as Bluetooth. Bluetooth devices have a lower battery drain than WLAN devices since they do not require complicated networking protocols as with WLAN. Furthermore Bluetooth is present, by default, on a variety of affordable mobile phones, whereas WLAN is available only on high-end smartphones and Personal Digital Assistants (PDAs). For this reason we chose Bluetooth as the RF technology on which to perform the location tracking. Bluetooth is a communication protocol rarely used in location tracking. This is due to the fact that Bluetooth is a peripheral wire replacement technology and as such it is not required to have signal strength readings which are meaningful to the user. Its use of power control to maintain consistent received signal strengths over increasing distances means that small changes in actual received signal strengths are not visible in the Received Signal Strength Indicator (RSSI) readings. Rather than using a standard Bluetooth AP which is available on most laptops, we chose to construct an AP using a Blueradios BRSC30N Bluetooth transceiver (Fig. 1). This hardware gives RSSI readings with resolution higher than that required by the Bluetooth specifications [2]. This permits higher resolution position discrimination than would be possible with typically available Bluetooth APs. Another reading which is available from this hardware when a connection is established is Link Quality (LQ). LQ is directly related to the Bit-Error-Rate of the Bluetooth connection. As illustrated in Section 4, LQ varies differently from RSSI throughout an indoor environment due to the method in which Bluetooth encodes data. This fact can be exploited to allow more distinct representation of different locations. RSSI and LQ are always available at the basestation computer, regardless of the connected mobile Bluetooth device. Depending on the connected device, other useful readings can be retrieved by the basestation computer via Bluetooth, which is why the selection of mobile device is of importance to our system design.

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3.2. Mobile devices In designing the localisation platform it is important to also consider the effect the choice of mobile device will have on the location prediction accuracy. When the basestation computer establishes a connection with a mobile phone, messages can be sent to the phone requesting information about the GSM network signal strength. This Cellular Signal Quality (CSQ) measurement, unlike RSSI and LQ, is related to the space between the phone and the distant cellular network basestation tower. This includes separation distance, number of wall obstructions and, unfortunately, outdoor environmental factors. The behaviour of this CSQ signal in our test environment will be illustrated in Section 4.1. Queries from the Bluetooth basestation for this CSQ reading has been shown to work, without any extra software installed, on most Bluetooth enabled Nokia and Sony Ericsson mobile phones. This covers a wide variety of mobile phones, including many relatively cheap models. Previous work has shown the value of incorporating this signal into location predictions [10]. When a more sophisticated, hence more expensive Nokia mobile phone is available it can be installed with a custom Python application. This application allows the acquisition of more accurate cellular network signal strength readings, along with the identification number of the currently strongest cellular network tower, or cell, which can then be returned to the Bluetooth basestation computer. This network cell identification number can change as the user moves throughout the environment and, as such, is also believed to be an indicator of location. We chose to use a Nokia 6230 to be representative of a commonly available and relatively cheap model of phone. A Nokia N95 is chosen to represent a more expensive model, capable of running our Python code, which exposes the extra cellular network readings. Besides slight differences in phone antenna radiation profiles, the availability of cell tower ID and different resolution cellular network signal strength are the only differences between these two models. Section 4 illustrates the behaviour of the cellular signal strength and cell tower ID signals throughout a test environment for the two mobile devices and Section 6 demonstrates how the availability of different cellular network signals influences localisation accuracy. 4. Data acquisition To appreciate how the available signals behave in an indoor environment the proposed localisation hardware was deployed in a section of a home environment. As illustrated in Fig. 2 the test environment consists of 4 rooms, designated (1) Bedroom 1, (2) Bedroom 2, (3) Bathroom and (4) Hallway. The goal of our work is to develop a system capable of correctly classifying each room given the available signals. 4.1. Static environment data To illustrate how each signal varies throughout the environment we initially obtained a signal map of the environment. To obtain this map the mobile phone is moved to a number of different fixed positions 1m above the ground and sampled for a period of two minutes at each position in a static, uninhabited environment. This results in over 100 samples of each signal for every position. Data from each position is labelled with x coordinate, y coordinate and room number. The sampled x and y coordinates are selected to form an even grid of squares throughout the environment, each square covering 1 m 2 . The mean of the samples at each position is assumed to approximate the real value of the signal at that position. Figure 3 demonstrates the behaviour of RSSI, LQ and Cellular Signal Quality (CSQ) throughout the environment. Unsampled locations are indicated by x’s. It can be seen that, while RSSI and LQ vary

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Fig. 2. Localisation hardware test environment.

Fig. 3. RSSI, LQ and CSQ signal maps for a Nokia 6230 throughout the test environment. Lighter shading indicates higher readings.

similarly throughout the environment, they are not entirely correlated and can be combined to give a more accurate indicator of location than either one alone. CSQ is the cellular network reading available from the Nokia 6230 without any extra software installed on the phone. RSSI, LQ and CSQ are all unit-less quantities. The Cellular RSSI (CRSSI) signal available from the Python interpreter enabled N95 is different from CSQ in that CRSSI has finer resolution. Also CRSSI is measured in dBm, which makes it a measure of actual received signal intensity. Figure 4 illustrates the CRSSI readings obtained throughout the environment when different cell tower IDs are detected. In this figure x’s indicate positions in which the given cell tower was not detected. From this we can see that certain cell towers are more likely to be

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Fig. 4. CRSSI signal maps for different detected cell tower IDs from a Nokia N95.

connected at certain locations, meaning that certain cell towers are strongest at certain locations. Hence the currently connected cell tower ID can be indicative of location. 4.2. Non-static environment data As in most localisation systems our location tracking is implemented in two phases. The first phase, known as the calibration or training phase, is where data representative of each location is obtained. In the second phase, known as the tracking phase, the localisation algorithm trained on the training phase data is used to predict location from the current set of samples. One issue with the method of data acquisition described in Section 4.1 is that training data obtained in this static way will not correspond to data obtained from real-life movements. For example, in the situation when a mobile phone is in a user’s pocket, the signals will be drastically different from when the mobile phone is placed 1m above the ground on a platform, due to signal attenuation effects of the human body and the dynamic behaviour of the signals when the device is moving. As a result, basing location predictions of a human carrying the mobile device on static training data will not be the most accurate technique, as will be highlighted in Section 6. To address this issue it was decided to also obtain training data from a user inhabiting each room for a short period of time. This is referred to as the “one-room-at-a-time” method. For each room an experimenter carried the mobile device in their pocket while the basestation computer logged 200 sets of signal samples, before changing to the next room. No other people were present when data was obtained. The experimenter walked to positions they would typically visit along paths they would usually take. Figure 5(a) shows how RSSI, LQ and CSQ vary throughout the environment for this training dataset. It is clear from the figure that data for Rooms 2 and 4 overlap, which highlights that 100% classification accuracy is not possible with these signals. This “one-room-at-a-time” method has a major drawback. It does not allow for the acquisition of data representative of a subject moving from one room to another, hence it will have reduced accuracy when this movement is occurring. For this reason it was necessary to experiment with a third type of training dataset. This dataset is obtained by constantly sampling while the experimenter walks throughout the environment. The experimenter makes a voice recording during the sampling process, noting the room transitions as they occur. Using this recording the samples can be labelled offline. This leads to a training

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Fig. 5. Training datasets obtained using the one-room-at-a-time and dynamic methods.

dataset which exactly matches real-life movement-based signal behaviours throughout each room. For comparison Fig. 5(b) illustrates how this so-called “dynamic” dataset varies throughout the environment. When comparing with Fig. 5(a) different coverage of the RSSI-LQ-CSQ space is observable. Clearly this will result in different classification accuracy for these different types of training data. Section 5 describes the algorithms employed in our work to infer the subject’s current location from these training datasets. Section 6 indicates the location prediction accuracy possible with these algorithms applied to the different datasets.

5. Location prediction algorithms Four classification techniques are compared for localisation accuracy; k-Nearest Neighbour (KNN), Linear Discriminant Analysis (LDA), Quadratic Discriminant Analysis (QDA) and Support Vector Machines (SVMs). 5.1. k-Nearest neighbour The KNN algorithm is a non-parametric classifier, which means that all predictions are calculated directly from the training data. It predicts the class of a test sample based on a majority vote of the class of the k training samples which are the smallest distance from the test sample. Distance estimates can be calculated by a variety of techniques, but typically an Euclidian distance measure is used. A seminal piece of WLAN location tracking work by Bahl and Padmanabhan [1] proposed approximating a device’s coordinate position by the mean of the positions of the k nearest training samples. Mantoro and Johnson [14] proposed using the KNN classifier to predict symbolic location, also based on WLAN technology. That work is similar to ours in that we’re interested in room-level location, which can be considered a symbolic location tracking problem. Matlab was used to implement the KNN algorithm. Based on findings by Mantoro and Johnson, a value of k = 10 was selected. The KNN algorithm is used as a baseline for comparing the other classification algorithms considered.

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5.2. Discriminant analysis LDA is a parametric classifier, which means that LDA classifications take place using parameters determined online from the training data. Then online class decisions are made based on maximum likelihood; D (x) = arg max (P (Ci |x)) , i

(1)

where Ci refers to class i and x is the set of measurements at a given instant of time. It is difficult to calculate P (Ci|x) directly from data, instead Bayes rule, P (Ci |x) =

P (x|Ci ) πi P (Ci )

(2)

is employed, where π i is the prior probability of class i and P (Ci ) is a normalising term which ensures all calculated probabilities are less than 1. P (x|Ci) for each class is estimated using a Gaussian function, hence our classifier is parameterised by the parameters of these Gaussians. For each class we estimate the mean and covariance matrix across all signals. To greatly simplify the estimation of the parameters for these Gaussians it is beneficial to assume the covariance matrices are equal across all classes. This results in linear classification decision regions for Eq. (1). The linear discriminant regions in LDA result in reduced discriminatory power for closely intermingled datasets. Hence it is necessary to consider a more flexible classifier, QDA. There are two ways of obtaining non-linear decision region boundaries for QDA. The first is to translate the inputs to a higher dimensional space using a polynomial, then perform LDA in this higher dimensional space. The second technique is to allow the covariance matrix parameters to vary across all classes. This results in more complicated Gaussian parameter calculations but also more sophisticated, non-linear, decision region boundaries. The second technique is preferred to the polynomial technique since it does not require the optimal selection of polynomial order. It has been shown that the classification flexibility is similar for both polynomial and Gaussian QDA [8]. We implement LDA and QDA in Matlab using the Discriminant Analysis Toolbox developed by M. Kiefte [11]. 5.3. Support vector machines LDA and QDA approximate the training data by Gaussian distributions and decide on the class of maximal probability based on these approximations. SVMs, on the other hand, create a decision boundary hyperplane which maximises the margin between the hyperplane and the vectors perpendicularly closest to the hyperplane (or support vectors). Then classification takes place using this hyperplane, which is a product of the support vectors and a vector of corresponding weights. The main step in training an SVM is to determine the subset of training dataset vectors which will become support vectors. This is performed using a type of optimisation called quadratic programming. In datasets where there is class overlap, like ours, it is necessary to include a slack parameter which permits some misclassifications with the aim of increasing the generalisation of the classifier. This slack parameter, denoted C , allows tuning of the importance of errors on the SVM optimisation. Hence, when C is a small value errors will have less of an influence on training accuracy, allowing higher levels of generalisation. As with any classifier, there is a trade-off between generalisation and accuracy, so this C parameter must be selected as part of the SVM optimisation process.

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Table 1 Nokia 6230 localisation accuracy for different training datasets Training dataset Static ORAT Dynamic

KNN 0.43 0.55 0.49

LDA 0.48 0.49 0.50

QDA 0.30 0.47 0.55

SVM 0.49 0.50 0.49

Table 2 Nokia N95 localisation accuracy for different training datasets Training dataset Static ORAT Dynamic

KNN 0.45 0.54 0.54

LDA 0.48 0.53 0.52

QDA 0.23 0.44 0.57

SVM 0.36 0.47 0.49

As with LDA, this SVM is capable of entirely discriminating between linearly seperable classes. Again for closely intermingled classes it is necessary to obtain non-linear decision region boundary hyperplanes. SVMs can be extended to perform non-linear classification using kernels. These kernels can be used to transform the SVM’s features into a higher dimensional space, similar to the technique of using polynomials with LDA introduced earlier. The kernel employed for our application is the commonly used Gaussian Radial Basis Function (RBF). This RBF kernel width (referred to as σ ) influences hyperplane smoothness and is also the subject of optimisation. The SVM algorithm is implemented using the simpleSVM package for Matlab [7]. The optimal parameter values σ and C are determined using a grid search based optimisation method to find the best validation accuracy. Validation accuracy is determined using 5-fold cross-validation on the training data, whereby the accuracy is a mean of the classification accuracies across all 5 folds. The optimised SVM is then applied to the test data. The next section shall present the localisation accuracy obtained by applying these techniques to experimental data. 6. Tracking accuracy To test the localisation accuracy achievable with the aforementioned algorithms it is necessary to obtain a test dataset. For each mobile device a test dataset was obtained for a 15 minute walk throughout the test environment. This walk was designed to represent the typical movements through a home environment. During the 15 minute walk data is constantly logged at the basestation computer. Also a voice recording of locations, much the same as that obtained during the dynamic data training phase, is obtained. The first factor to consider when developing a room classifier is the appropriate type of training data. The three available types of training datasets, as described in Section 4, are Static, One-Room-at-a-Time (ORAT) and Dynamic. The accuracy obtained with each of these training datasets using each of the prediction algorithms on the test dataset is summarised in Tables 1 and 2. Each entry in these tables is the mean accuracy for the given dataset, trained using the given classifier and tested on the test data. The mean accuracy is the mean of each individual room accuracy. The individual accuracy for each room is calculated as the number of correct predictions for each room expressed as a ratio of the total number of test samples obtained in that room. From the tables it is evident that the highest accuracy is obtained by using the dynamic training dataset. This is intuitive since this dynamic training dataset is obtained in the fashion most similar to the test dataset. This indicates that,

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Fig. 6. Classification accuracy with increasing filtering for dynamic training data.

unlike in traditional localisation work which uses ORAT type training datasets, slightly better accuracy can be obtained by using training data obtained from a subject performing their usual movements. As in early iterations of our work [10], filtering can be used to reduce the number of misclassifications due to the measurement noise which is inherent in the low-cost Bluetooth protocol. A simple running average windowed filter is utilised over N samples. Figure 6 illustrates the increase in accuracy resulting from increased filtering window lengths for the dynamic training data classifiers. From these figures it is clear that the accuracies for KNN, LDA and QDA exhibit monotonically increasing accuracy with increasing filtering window length. This confirms that reducing measurement noise has the effect of increasing localisation accuracy and that the reduction of measurement noise in Bluetooth is achievable using one of the simplest types of filters. QDA outperforms LDA and the more traditional KNN algorithm. Unexpectedly SVMs perform poorly for this localisation problem. SVMs exhibit higher classification accuracy than other classifiers when the level of similarity between datasets is high [10], due to its ability to fit training data so well. However, SVMs’ lower generalising ability results in reduced localisation accuracy when compared to other, less complex classifiers. To further highlight the suitability of dynamic training data over static or ORAT data, the same comparison as before was conducted, except with 10 sample window filtered data. The findings are summarised in Tables 3 and 4. From these results it can be observed that filtered static training data provides worse performance than when unfiltered data is used, indicating that the noise independent static data is not indicative of location during real-life motion. Also ORAT training data only has moderately improved localisation accuracy, indicating that its noise free component is no more representative of location than the original signal. Of greater significance is that for dynamic training data, accuracy has greatly increased for all classifiers in the absence of measurement noise. Across all 4 classifiers and both mobile devices, filtering results in an average increase of 26.8% in classification accuracy using dynamic training data. The main downside to filtering data in this manner is that it introduces prediction latency when room transitions occur. An even more critical issue is when two room transitions occur within the 10-sample filtering window the intermediate room has its samples mixed into samples from the preceding and succeeding rooms. This results in lower prediction accuracies for these intermediate rooms. However, assuming these intermediate rooms are rarely occupied for long periods of time they can be safely assumed to have negligible effect on the long-term localisation accuracy. For example, if we remove the

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Table 3 Nokia 6230 localisation accuracy with 10-sample window filtered data Training dataset Static ORAT Dynamic

KNN 0.47 0.57 0.64

LDA 0.30 0.54 0.65

QDA 0.33 0.51 0.69

SVM 0.35 0.58 0.64

Table 4 Nokia N95 localisation accuracy with 10-sample window filtered data Training dataset Static ORAT Dynamic

KNN 0.48 0.55 0.65

LDA 0.29 0.52 0.65

QDA 0.23 0.49 0.71

SVM 0.32 0.51 0.61

Table 5 Overall accuracy when omitting the effect of the transition room on mean accuracy calculation Test device KNN LDA QDA SVM Nokia 6230 0.81 0.84 0.80 0.78 Nokia N95 0.80 0.84 0.83 0.75

effect of the hallway on the mean localisation accuracy we can see much higher localisation accuracy, as evident in Table 5. These findings indicate that in terms of localisation accuracy neither mobile device stands out as significantly better. The presence of higher resolution CRSSI and cell tower ID readings do little to increase the localisation accuracy for the N95. In terms of cost and device availability, the Nokia 6230 device is a better choice. However, the N95 with its Python interpreter, allows software to access almost all components of the mobile device, allowing the addition of functions which can utilise any of the phone’s hardware including accelerometers, cameras and network connectivity. These additional components can provide further functionality in a home monitoring situation like fall detection, with little extra hardware cost, especially when considering the falling prices of these mobile devices as newer, more complicated devices are released. 7. Conclusions Smart homes have previously been shown to aid elders and permit extended life at home, resulting in increased quality of life. This work contributes to allowing location-based smart home systems to be more readily deployed into an already built home by developing an extremely minimal infrastructure room-level localisation system. This localisation technique can be readily integrated with any number of location-based smart home applications which already utilise Bluetooth. Two different types of mobile devices were compared and it was found that both provided similar levels of accuracy. Where cost is an issue and a prompting system is not necessary, or already exists within the environment, a cheaper device like a Nokia 6230 can be used with similar localisation accuracy. However, when prompting and localisation functionality is desirable in the same device, the N95-type device is more suitable, due to its more stable programming environment. Along with the ability to

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reliably provide the prompts to the user, the N95 has further sensors available to it which would not be available to the simpler Nokia 6230 device. These sensors can be used for any number of monitoring activities; not least for detecting whether the mobile device is currently being carried by the subject, confirming the impact of a prompt at any given time. LDA and QDA has been shown to provide improved localisation accuracy over the commonly employed KNN algorithm. SVMs, in spite of their flexible classification abilities, have been shown to perform worse than expected due to their inability to generalise sufficiently on the training data. It has also been shown that training classifiers on entirely dynamically obtained training data provides the highest localisation accuracy and that omitting the transition room from the location predictions provides increased accuracy.

8. Future work The main focus of future work is to increase the accuracy of the classification techniques using a priori knowledge about the motions of an elder throughout an environment on a given day. This can be implemented using a Hidden Markov Model to modify the class probability at every time step using room transition probabilities. A major disadvantage of this and many other RF tracking techniques is that it requires a piece of hardware to be on the subject’s person at all times. As such, another important focus of future work is on developing techniques to detect and address the situation when the mobile device is not on the elder’s body. The array of sensors available in these mobile devices can help. For example in the N95 accelerometers can be used to detect when the device is moving. Also the camera and ambient light sensors can be used to determine if the device is in the subject’s pocket. Hence, if the device is in a pocket and moving, tracking can successfully occur. If it is out of the pocket and not moving, then it is most likely lying down somewhere. If this is the case the elder can be prompted to confirm that they are carrying the device. They will be prompted gently at first in case the detection was erroneous. Prompts will get louder and placed at further environmental displays, if available, as time passes without a confirmation. It is also beneficial to detect that the phone is out of the pocket and moving, for example when it is in the subject’s hand. In this situation we know that location predictions will be less accurate because the RF waves are not subject to the same propagation mechanisms as when the device is in a subject’s pocket for example. Naturally, forgetting to carry the mobile device is not an issue for a PIR localisation system, since a mobile device is not necessary when using PIR sensors. However part of the motivation of our work is to address the defficiencies of PIR localisation for multiple occupancy environments. When multiple subjects are in a PIR localisation environment multiple location predictions are impossible. Whereas in an RF localisation environment multiple location predictions are possible, albeit with reduced accuracy. As a result, future work seeks to deploy a PIR localisation system in the test environment to fully quantify the improvement in long-term localisation accuracy possible with our system in a realistic environment. Also one further reliable localisation technique must be deployed to permit accurate multisubject localisation. This higher accuracy will be at the cost of a more expensive system but is necessary to validate our system’s performance. This will allow us to study the long term movements of subjects to determine more suitable home tracking accuracy metrics, akin to ignoring the rarely occupied hallway in Section 6 and Table 5. This is envisioned to lead to better ways of evaluating system performance, enabling future designs to be optimised for home tracking situations.

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Acknowledgements This work is funded by the Irish Research Council for Science, Engineering and Technology (IRCSET) under their Embark Initiative. Grateful acknowledgement is also due to Adrian Burns, Julie Behan, Mick McGrath and Lorcan Walsh for their invaluable technical input. References [1] [2] [3] [4] [5] [6] [7] [8] [9] [10] [11] [12] [13] [14] [15] [16] [17] [18] [19] [20] [21] [22] [23]

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