A Hierarchical Hidden Markov Model to Support Activities of DailyLiving with an Assistive Robotic Walker

Mitesh Patel, Jaime Valls Miro and Gamini Dissanayake

Abstract— This paper proposes a Hierarchical HiddenMarkov Model (HHMM) framework as the most suitable toolto exploit the interactions between an intelligent mobility aidand their human operator. The framework presented is capableof learning a mixed array of the Activities of Daily Living(ADL) that the typical user of these supportive devices wouldnormally engage in, both navigational and non-navigationalin nature, and provide assistance as and when required.The main contribution of this paper is the demonstration ofhow this probabilistic tool capable of modelling behavioursat multiple levels of abstraction is a natural embodiment ofmachine intelligence to support user activities. Effectiveness ofthe proposed HHMM framework is evaluated with a number ofhealthy volunteers using a conventional rolling walker equippedwith sensing and navigational aids whilst operating in astructured environment resembling a home. A comparison withmore traditional discriminative models and mixed generative-discriminative models is also presented to provide a completepicture that highlights the benefits of the proposed approach.

I. MOTIVATION

Demographic projections show that the world’s agingpopulation is rapidly increasing: the worldwide proportionof people aged over 60 is expected to double between2000 and 2050 [1]. The challenges associated with healthylongevity are driving the need for improvements in the rangeof services related to aged care. Various options are beingcanvassed on the use of emerging innovative technologies,such as telemedicine, elderly-friendly housing, remote moni-toring systems and sensors, or wander management systems.The concept of machines assisting people is pervasive inmany technological tools in use today, and studies haveshown that by gaining access to such assistive devices, theelderly and the frail can benefit substantially as they providea vehicle for sustaining social interactions and physical andmental activity for longer, thereby facilitating their ability tolive more independently and consequently improving theiroverall quality of life [2]. Conventional powered wheelchairsor walking aid devices (”walkers”) for instance have suc-cessfully provided the means by which many frail olderadults, and a variety of other people with gait disorders, canmaintain mobility and functional independence to perform alarge array of Activities of Daily Living (ADL).

According to the WHO, assistive technology is definedas ’an umbrella term for any device or system that al-lows individuals to perform task they would otherwise beunable to do, or increase the ease and safety with which

M. Patel, J. Valls Miro and G. Dissayanake are withFaculty of Engineering and Information Technology, Universityof Technology Sydney, 15 Broadway, Ultimo, Australia(mnpatel,javalls,gdissa)@eng.uts.edu.au

task can be performed’ [3]. This expression reflects anappreciation of the expanding ways technology can supportthe independence of the older and disable population [4].This has motivated researchers to develop more advancedtechnological aids, such as robotic wheelchairs and walkersor smart canes, capable of providing enhanced support topeople suffering from mobility impairments due to age,injury or disease (e.g. dementia) [5].

Yet growing advances in machine intelligence (togetherwith the availability of low-cost sensing and computingplatforms) have now made it feasible to progress this conceptfurther, and the idea of assistive intelligent robotics has nowemerged as a promising field of development to narrow thegap between an individual’s ability and their environment.For instance, ageing-in-place supported by smart intelligentmachines has the potential for substantial savings in resi-dential aged care, to provide early alerts to changing healthpatterns or to minimise falls and other accidents in thehome [6]. To achieve this degree of interaction, user and in-telligent machine need to coexist and operate collaborativelyin response to the user’s needs, as opposed to the looselycoupled man-machine interactions normally encountered inmore traditional autonomous robotics systems.

In this work we propose an intelligent collaborative roboticsystem based on a unified probabilistic model capable oflearning and inferring user’s interactions. This is achieved atvarious levels of abstraction as the natural way to understandand comply with the user’s intended behaviours while safelyovercoming some of their physical limitations. The user isalways in control yet the system is designed to unobtrusivelyassist the users actively as they go about their normal dailyactivities without the need for any explicit actions suchas pushing buttons/panels or voice commands. This is animportant motivation for this work, since it is assumed theintended user population may not always be cognitively orphysically able of pro-actively providing such unequivocalindications of their desires. Furthermore, the hierarchical andtemporal nature of the proposed framework means the modelis able to understand both instantaneous (short term) andlong term (end goal) intentions that make up the traditionalspectrum of ADLs (i.e. ’standing up’ vs ’going to thekitchen’ for instance).

The model has been experimentally evaluated with atraditional walking frame equipped with actuators, embeddedcomputing and sensing (e.g. posture of the user, pressureplaced on handle bars, robot speed and orientation, etc.).Motion histories and patterns of behaviour from a suiteof healthy volunteers have been extracted and the data

The Fourth IEEE RAS/EMBS International Conferenceon Biomedical Robotics and BiomechatronicsRoma, Italy. June 24-27, 2012

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used to validate the proposed model as a demonstration ofthe effectiveness of the algorithm in inferring user’s ADLintentions, the first step towards the full picture of an all-round user-driven intelligent assistive machine.

II. RELATED WORK & PROPOSITION

To develop an integrated systems which involves interac-tion between human and robots/machine, it is necessary thatthe cooperation provided by the robot/machine is perceivedas equal to that provided by the human. To achieve thislevel of human-robot integration, a mechanism is needed sothat the user’s patterns of behaviour can be learnt as a firststep to be able to infer user’s intentions. Given the inherentlevel of uncertainty surrounding the indications given by thefrail and elderly population, and the sensors used to observethem, it is difficult to model these activities in a deterministicmanner. Stochastic or probabilistic models are the techniquesof choice that researchers have explored to represent thepossible uncertainties involved. They have been successfullyused by the AI community in particular in order to representcomplex systems with graphical models [7]. Taha et. al. [8]used a Partially Observable Markov Decision Process model(POMDP) as an intelligent decision-making agent to assistwheelchair users in their daily navigational activities, whereaccouting for uncertainty in the actions was required to beable to model users with physical impairments commandingthe wheelchair. The framework relied on minimal user inputsfrom the wheelchair joystick, in conjunction with the learnedPOMDP, to estimate and subsequently drive the user to hisdestination. Wasson et. al. [9] developed the COOL-Aidesmart walker to operate in a more tightly coupled, sharedcontrol loop with its human user. Instead of active guidance,the COOL-Aide provides a passive shared control systemthat delivers active steering assistance only as needed, andno propulsion assistance. The main purpose of this systemis to derive the navigational intention of the user basedon measuring forces and moments applied to the walker’shandles. Omar et. al. [10] proposed an activity recognitiontechnique based on Hidden Markov Models (HMMs) andConditional Random Fields (CRFs) for an instrumentedpassive rollator walker. The model recognised a number ofuser states: not touching the walker, stop/standing, walkingforward, turn left, turn right, walking backwards and transfers(sit to stand/stand to sit). Glover et. al. [11] proposed aHierarchical Semi-Markov Model (HSMM) to infer user’swalking activities, which were modelled at three differenthierarchical levels based on metric coordinates, topologicallocations and the logical activities that the user was perform-ing at those locations.

Outside the realm of robotic walkers and wheelchairs,probabilistic models have also be employed in a variety ofother relevant applications where uncertainty in the sequenceof actions and observations play an important role in theestimation process. In the area of ubiquitous computing forinstance, Liao [12] used an HHMM framework to infer user’smode of transportation, destination location and predict bothshort and long term movements. The framework was also

Fig. 1. Hierarchcical-DBN representation of a 2-level HHMM Framework.The horizontal dashed lines indicate levels of hierarchy

able to infer if the user was deviating from his normalactivities as an indication to provide guidance cues. In thearea of dialogue act and speech recognition, Sunendran andLevow [13] used a combination of Support Vector Machine(SVM) and Hidden Markov Model (HMM) for taggingdialogue acts. Higher classification accuracy was reportedusing the hybrid HMM-SVM model as compared to using apure SVM or HMM model.

It is important to note how in particular in the context ofrobotic walkers, the various frameworks proposed [9], [10],[11] were aimed at predicting either short term (local) or longterm (navigational) goals, but not both. In this scenario, theuser activities have been broadly categorised as navigationalor non-navigational. Navigational activities are termed ’long-term’ goal-specific tasks that a user would perform to reacha target location. For instance in a given home environment,a walker user would generally have a well known set oflocations that they visit during their daily activities, e.g.kitchen, bedroom, bathroom, laundry, etc. Alongside thesepurely navigational activities, the user would also needsupport to perform other tasks, broadly classified as ’short-term’ in nature, e.g. standing up, sitting down, or the abilityto send the walker away when not needed. The hierarchicalnature of the work hereby proposed is a successful attemptto overcome the need for this distinction by integrating arepresentation for the sequential nature of some of thesetasks, thereby facilitating learning and inference of typicalADLs within a unified probabilistic framework in the formof a Hierarchical Hidden Markov Model (HHMM).

The rest of the paper is organised as follows. Section IIIreviews the basics of HHMMs. Section IV provides anoverview of the application-specific HHMM framework. Sec-tion V describes the experimental setup and methodologiesused for the data collection, while Section VI discussesthe results obtained from a set of experiments on healthysubjects. Section VII summarizes the proposition of the paperand looks at plans to further this work.

III. HIERARCHICAL HIDDEN MARKOV MODELFRAMEWORK

Hierarchical Hidden Markov Model (HHMM) are struc-tured multi-level stochastic processes. The HHMM is an

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extension of HMM that is designed to model domainswith hierarchical structure and/or dependencies at multiplelength/time scales [14]. The strings emitted by abstractstates are themselves governed by sub-HMMs, which can becalled recursively. When the sub-HMM is finished, control isreturned to wherever it was called from [15]. The hierarchicalnature allows decomposition of the problem at differentlevel of abstraction thereby facilitating exploration (longterm planning/intention) and exploitation (short term plan-ning/intention) within the same framework. In the paradigmof activity recognition, the high-level activities call the morerefined low-level activities according to some distribution.A low-level activity will in turn call another lower-levelactivity, and this process continues until the most primitivepossible activity is performed. When the lower level activityterminates - in some state - then the parent behaviour mayalso terminate so long as the current state is in the set ofdestination states of the parent node.

A. Representation

An HHMM framework can be represented as a Hierarchi-cal Dynamic Bayesian Network (H-DBN) as shown in Fig. 1.The structure comprises of three types of nodes, Qd

t ,Ot ,Fdt

where d is the depth of the hierarchy (d = 2 in our case).Edges between nodes represent their dependencies on eachother. The details of each node is specified as follows:

• Qdt represents the state of the system at time t and

level d. Note that at any given time the system willbe probabilistically represented by the state belief at alllevels, and so will be the user goal state at the top level.

• As the true state of the user is hidden, observationsnode Ot are required which provides user/environmentinformation. These nodes can be modelled as a mixtureof Gaussian (µ,Σ) or as discrete P(Qd

t /Ot) node.• Fd

t is the terminating state which specifies the naturalcompletion of a sub-HMM and return the control backto the higher level/parent states.

Given the parameters (Qdt ,Ot ,Fd

t ), the H-DBN defines thejoint distribution over the set of variables that representsthe evolution of the stochastic process over time. Thesedistributions are in the form of prior distributions (initialprobabilities), the transition probability model and the obser-vation probability distribution model. The prior distributionand the transition model are defined at every level (d).

B. Prior Model

The prior provides the initial probabilities or the mostlikely initial state of the user. The initial probabilities at boththe levels are defined by

P(Q21) = π2( j)

P(Q11) = π1

k ( j)(1)

C. Transition Model

Each node in the HHMM represents a conditional prob-ability distribution (CPD) or table (CPT). The state of thehighest level (level 2 in Fig 1) at time t, depends upon the

(a) Sub-states of HHMM to go to kitchen

(b) HHMM to perform Standup ActionFig. 2. (a) Long term high level navigational activity of going to kitchendivided into sub-activity of going to Jn7-Jn8-Jn2 and then to goal state. (b)Short term non-navigational activity of standing up does not have any substates and hence returns to the end state after its execution.

previous state at the same level and the termination flag attime t −1. Probabilities at the highest level are defined by

P(Q2t = j|Q2

t−1 = i,F2t−1 = f ) =

{A2(i, j) if F2

t−1 = 0π2( j) if F2

t−1 = 1(2)

Similarly, the states at the intermediate level (level 1 inFig. 1) at time t, depends upon the previous state at the samelevel and the termination flag at time step t −1 and the stateat the higher level in the same time step t, the probabilitiesof which are defined in (3). The termination state F at timet depends upon the level 2 state and level 1 state in thesame time step t. The distribution of the termination stateare defined by (4).

P(Q1t = j|Q1

t−1 = i,F2t−1 = f ,Q2

t = k) ={

A1k(i, j) if F2

t−1 = 0π1

k ( j) if F2t−1 = 1

(3)

P(F2t = 1|Q2

t = k,Q1t = i) = A2

k(i,end) (4)

A2 and π2 represent the transition and initial probabilitiesrespectively at level 2 where as A1

k and π1k represents the

same at level 1 given the state at level 2 is k.

D. Observation Model

The observation model signifies the probability of seeinga specific observation conditioned on a discrete hiddenstate. For our application, observations are modelled as bothGaussian and discrete. The CPDs for Gaussian and discretenodes is given by

P(Ot |Q1t = i) = N(µi,Σi)

P(Ot |Q1t = i) =C(i)

(5)

E. Learning and Inference

Learning in the context of probabilistic models meansadjusting the model parameters to better fit the training data.Various learning techniques both supervised and unsuper-vised can be used for learning the HHMM model. Expecta-tion Maximisation (EM) and its variants is one of the mostpopular statistical technique used for unsupervised learning.In realistic scenarios it is often difficult to obtain labelleddata, and unsupervised modes of learning are preferable. Thisis also the case for this work where tagging “behaviours”is not a straighforward task. Thus, EM was employed forlearning the model and a maximum likelihood estimator to

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(a) Task Performed by user for Data Collection (b) Rollator WalkerFig. 3. (a) 2-D Bird’s eye view of environment divided into typical locations of interest in an home, superimposed with navigational and non-navigationalroutines performed by one of the walker users, (b) Rear view of the instrumented power rollator walker

predict user activities. The algorithm iterates between anExpectation step (E-step) which estimates the expectationsover the hidden variables using the observations along withthe conditional probability density (CPD) of the model, and aMaximization step (M-step) in which the model parameters(i.e. the CPD’s) are updated using the expectations of thehidden variables obtained in the E-step.

IV. PROBLEM SPECIFIC HHMM FRAMEWORK

In this application, user’s activities are hierarchically split-up at different level of abstraction. The goal state/activitythat the user wants to perform is represented at the top levelwhereas at the intermediate level (level 1 in Fig 1) representsthe sub-sequence states of the goal level. If a goal state doesnot have any sub-sequence then the state at the intermediatelevel will be the same as the goal state. To better illustrate thisconcept, the example in Fig. 2(a) represents the case whenthe user wants to go to the kitchen from the bedroom. As perthe topological map shown in Fig. 3(a), this activity can besubdivided into a string of smaller navigational sub-activities:the user will come out of the bedroom, go to junction7, then to junction 8 before he/she reaches the intendedfinal destination. For non-navigational activities there are nointermediate sub-states which the user needs to go throughin order to complete that activity, as per the example shownin Fig 2(b) the activity of ”standing up” being the mostprimitive action cannot be divided into sub-sequence. Hencethe intermediate state, instead of being a string of sub-states,has the same intermediate state as the top level before itterminates.

For our experiments we broadly classified everyday useractivities as per those in Table I. These activities are a com-bination of non-navigational activities performed at a singlelocation along with goal oriented navigational activities.

As shown in Fig. 1, HHMM framework observations area combination of both the physical sensor installed on thewalker platform, and localization information:

• 4 Gaussian nodes are the readings from the physicalsensors installed on the walker (IRt, IRw, LSG, RSG).

• 2 discrete nodes, RF and Localization. An RF switchis used by the user to indicate the walker to go away

and recall as needed. Location is derived from a lo-caliser in a topological manner: topological maps havesuccessfully been used in the past [8] as they providesa more compact representation of the environment thanmetric maps. Hence, Instead of using the metric (x,y)information provided by the localiser, 19 discretisedlocations are supplied as an observation to the HHMMnetwork.

As shown in Fig 3(a), our office environment was consid-ered as a representation of a typical home environment, andthe geometrical space was divided into the relevant points ofinterest the user will normally visit during the day.

V. EXPERIMENTAL SETUP

A. The Active Walker Rollator Paltform

The power walker employed for data collection is shown inFig. 3(b). It is a modified commercial rollator walking framewith four wheels. The base frame has been instrumented with24Volts (DC) reversible gear-head motors, rotary mechanicalcouplings and incremental optical encoders to the two rearwheels (front wheels are passive). Four infra-red (IRs) prox-imity sensors were used to detect the presence of the user(two) and to perform gait analysis (other two). The pair ofIR sensors used for gait analysis were installed 10 cm offthe ground so that it would measure the distance betweenthe walker user’s legs. Four strain gauges (SGs) (2 on eachhandle bar) were used to measure the pressure a user wouldbe exerting while handling the walker. The differential forcesbetween the vertical and horizontal axes in each handle-baris indicative of the users’ readiness to start a task (sittingdown, standing up or ambulation steering).

TABLE IBROADLY CLASSIFIED USER ACTIVITIES

User Activity Type of ActivityGeneral Assistive Navigation (GAN) non-navigational

to Living Room (LRO) goal oriented navigationalto Kitchen (KIT) goal oriented navigational

to Bathroom (BAT) goal oriented navigationalto Bedroom (BED) goal oriented navigationalto Laundry (LAU) goal oriented navigational

Walker Go Away (WGA) non-navigationalRecall Walker (RW) non-navigational

Stand Up (SU) non-navigationalSit Down (SD) non-navigational

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The hardware also includes a radio switch, which indicatesthe walker to come back from parking position to whereit last left the user, or vice-versa. This feature is moreadvantageous in certain locations such as the living room,or bedroom where the user spends more time unaided. Ontop of the sensing hardware the walker was also instrumentedwith a low-level micro-controller to communicate with thesensors and a high-level control computer for data processingand storage.

B. Data Collection

As the authors did not have access to aged/frail populationto perform the set of experiments that could fully validatethis work, healthy volunteers were recruited from the localcommunity. As per the results presented by Zong et.al. [16],gait patterns such as stance/swing phases and speed ofhealthy users are different from those encountered in theold/frail population. Hence in order to obtain meaningfuldata with representative gait characteristics from healthyvolunteers, the experiment described in Section V-B.1 wasfirst carried out. Data was thus collected from three healthysubjects who did not have any technical background, onemale and two female (25-30 years of age). Volunteers werebriefed and were also given time to practice using the walkerso as to understand its functionality. Notwithstanding thisapparent limitation, it is not detrimental from the ability ofthe proposed approach to infer generic behaviours from agiven set of ADL user data, as will be shown in Section VI.

1) Gait Analysis Data: In this experiment the user wasasked to walk twice on a straight path for 10m on a flatsurface using the power walker. In the first part the userwas asked to walk at their normal everyday walking pace,whereas in the second part the users were asked to walkthe same distance and path with the speed of the walkercontrolled so that the maximum speed at which user couldwalk was set at 0.3m/sec. The gait characteristics achievedusing this speed was similar to that of a typical frail andelderly user of a mobility assistance platform as reported inthe literature [16]. The user’s gait dynamics was measuredusing the two IR sensor installed beneath the walker.

2) Everyday Activity Data: In order to assist peoplein performing their everyday activities, it is important tounderstand the activities and patterns a user might performor follow to accomplish a given task. In the scope of thisproject, we predefined some of the many everyday tasksa typical walker user would normally encounter, althoughunder clinical tests these patterns would be normally definedwith the help of an occupational therapist [17]. Once defined,data was collected from the sensors while the user would visit5 locations of interest (as shown in Fig. 3(a)). For example,the sequence of activities that a user would perform to goto the living room from the bedroom, would be recalling thewalker RW, followed by standing up SU using the support ofthe walker, then geting out of bedroom and walking towardsthe living room LIV. Once in the living room the user wouldsit down SD and tell the walker to go away WGA so thatit is not a hindrance.The sub-activities involved during the

TABLE IITEMPORAL-DISTANCE GAIT PARAMETERS FOR EACH USERUsers User 1 User 2 User 3

Gait Parameters Normal Speed Limit Normal Speed Limit Normal Speed LimitWalking Walking Walking Walking Walking Walking

Stance Phase (sec) 0.46 0.96 0.58 1.17 1.30 1.63(39.95%) (80.26%) (41.20%) (81.59%) (70.29%) (82.28%)

Swing Phase (sec) 0.78 0.24 0.74 0.28 0.33 0.35(60.05%) (19.73%) (58.79%) (18.40%) (29.71%) (17.71%)

Avg. Speed (m/sec) 0.52 0.16 0.55 0.19 0.38 0.25

navigational task would then be to traverse towards jn 7,followed by jn 8 and jn 11, before the user reaches theliving room.

VI. EXPERIMENTAL RESULTS

The HHMM framework was tested off-line using the realtime data collected as described in Section V-B.2. User datawas logged using Player/Stage open source toolbox [18] ata sampling rate of 0.5 Hz.

A. Gait Analysis

By setting an appropriate maximum walker speed, usergait’s characteristics (Table II) were found to be in agreementwith those reported in the literature [16]. The temporal-distance gait parameters reflects the persons’ dynamics dur-ing walking. Gait parameters (as reported in [16]) wereextracted to analyze the gait patterns of the user for thecollected data as described in V-B.1. The user spent moretime in the stance phase as compared to the swing phase,and the walking speed was also reduced when comparedto their normal walking gait parameters. The results ofthis experiment substantiates the fact that by controllingthe maximum speed of the walker, the data logged for theADLs inference experiments from the healthy subjects canbe assumed to closely correlate with the gait characteristicsof an old/frail person. Furthermore, the variation in the user’sgait dynamics when controlling the speed of the walker ascompared with their normal walking pattern was also foundto be in close correlation to that reported in [16].

B. Inferring User Activities

The HHMM framework was tested off-line using real timedata. We used the BNT toolbox [15] to learn and infer userintentions using the proposed HHMM framework. Unsuper-vised learning in the form of Expectation Maximization wasused to learn user activities, and the Maximum LikelihoodEstimator was used for inference. The data was manuallylabelled for cross validation and was divided in two equalsets for training and testing purpose. The inference accuracyattained using this framework was in the range of 98%. Forcompleteness, we also compared the inference accuracy ofHHMM framework with a single level DBN as shown inTable III. As expected, the overall inference accuracy of thesingle level DBN was much lower, around 53%.

C. Comparison with Discriminative models

We also compared the accuracy of a discriminate SVMclassifier and a hybrid HHMM-SVM model. HMM-SVMhybrid model has recently been successfully used in anumber of application such as speech recognition, where theexcellent discrimination performance of SVM compliments

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TABLE IIIINFERENCE ACCURACY OF GENERATIVE AND DISCRIMINATIVE MODELS (IN PERCENTAGE)

Goal-oriented Navigational Activity Non-Navigational ActivityModel/Activity tobed tokit tobat toliv tolau RW WGA SU SD GAN Overall

DBN 66.40 12.45 10.8 20.9 47.02 100 96.36 100 100 99.11 53.20SVM 44.88 7.87 31.66 94.18 25 100 100 98.07 100 99.70 69.82

HHMM 100 100 87.5 100 95.23 100 98.14 100 100 97.94 98.15HHMM-SVM 100 100 100 100 100 100 100 98.07 98.07 94.72 98.88

the temporal modeling properties of HMM to provide ahigher inference accuracy [13]. In our work, a static SVMwas compared with a hybrid HHMM-SVM model, whereactivities were first discriminated between being naviga-tional, or belonging to one of the non-navigational tasks. Themodel then exploited the temporal relations of the HMMMframework for the navigational tasks in the same fashion asbefore to infer the intended user destination. Around 70%classification accuracy was achieved using plain SVM, inagreement with what was reported in our earlier work [19].

However, the inference accuracy of the hybrid HHMM-SVM jumped to around the same values as the proposedHMMM, close to 99%. This further validates the proposedhierarchical structure to be able to take full advantage ofthe temporal/topological nature of ADLs. Yet by employingHMMMs there is the significant advantage of not havingto resort to (partial) supervised learning. This appears as asignificant advantage for the target audience, as the physicalabilities of the intended users will deteriorate with age, andthe network benefits itself from the capacity to adapt to ADLschanges without the need to expensive data tagging.

VII. CONCLUSION AND FUTURE WORK

In this paper we propose the use of hierarchical temporalmodels as a tool to provide assistance to users of mobilityaids while carrying out their Activities of Daily Living. Thework is evaluated with real data obtained from users of anintelligent robotic rollator walker. An HHMM frameworkis proposed as the decision making algorithm, attainingaccuracies in the order of 98%. HHMM models have beencompared with other techniques, such as a single levelDBN model, a static SVM classifier and a hybrid HHMM-SVM combination model. The results show how typical non-navigational activities carried with the mobility device, e.g.RW, WGA, SU, SD, GAN, are similarly predicted by allthe various models. However, prediction accuracy for thenavigational states is almost halved for the DBN and SVMmodels, a further proof that temporal information plays acritical role in predicting long term intentions, and thesenetworks are not capable of modelling those relations. Onthe other hand, the proposed hierarchical network is able toexploit these with the aid of intermediate navigational cues.Despite the slightly higher inference accuracy of the HHMM-SVM hybrid model, the authors advocate the usage of theHHMM model for two critical reasons: firstly, given the factthat the physical functionality of frail and elderly people willdecline with age, the ability and patterns of the intendedaudience will constantly changes and hence it will be ratherdifficult and costly to obtained (partially) labelled data asrequired to train an SVM in the HHMM-SVM hybrid model.

Secondly, the HMMM model opens the door to incorporateunsupervised online learning algorithms when novelty in theuser patterns occur (e.g. new places of interest that need tobe incorporated into the model), such as online-EM withinthe HHMM framework, a work currently underway.

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