=Paper= {{Paper |id=Vol-2061/paper4 |storemode=property |title=Experimental Analysis of Deep Echo State Networks for Ambient Assisted Living |pdfUrl=https://ceur-ws.org/Vol-2061/paper4.pdf |volume=Vol-2061 |authors=Claudio Gallicchio,Alessio Micheli |dblpUrl=https://dblp.org/rec/conf/aiia/GallicchioM17 }} ==Experimental Analysis of Deep Echo State Networks for Ambient Assisted Living== https://ceur-ws.org/Vol-2061/paper4.pdf

Experimental Analysis of Deep Echo State
Networks for Ambient Assisted Living

Claudio Gallicchio ( ) and Alessio Micheli

Department of Computer Science, University of Pisa,
Largo B. Pontecorvo 3, Pisa, Italy
gallicch@di.unipi.it, micheli@di.unipi.it

Abstract. The Reservoir Computing (RC) paradigm represents a state-
of-the-art methodology for efficient building of recurrent neural networks,
which in the last years has proved effective in learning real-world tempo-
ral tasks from streams of sensorial data in the Ambient Assisted Living
(AAL) domain. Recently, the study of RC networks has been extended
to the case of deep architectures, with the introduction of the deep Echo
State Network (DeepESN) model. Featured by a layered composition
of recurrent units, DeepESNs are inherently able to develop a hierar-
chically structured representation of temporal information, at the same
time preserving the RC characterization of training efficiency.
In this paper, we discuss the introduction of the DeepESN approach in
the field of AAL. To this aim, we perform a comparative experimental
analysis on two real-world benchmark datasets related to inferring the
user’s behavior from data streams gathered from the nodes of a wireless
sensor network. Results show that DeepESNs outperform standard RC
networks with shallow architecture, suggesting a multiple-time scales na-
ture of the involved temporal data and pointing out the great potentiality
of the proposed approach in the AAL field.

Keywords: Deep Learning, Reservoir Computing, Deep Echo State Net-
work, Ambient Assisted Living, Human Activity Recognition

1 Introduction

Being able to recognize the behavior of humans in their every-day environments
is one of the key objectives of Ambient Assisted Living (AAL) applications. This
ability can be indeed exploited in diverse applicative contexts aiming at improv-
ing the quality of life of older people, e.g. by monitoring the regularity of their
activities, enhancing the degree of personalization of smart home services based
on their habits, or anticipating their needs in the place where they live or work.
Among the possible solutions, the use of wireless sensor networks (WSN) [10] as
a mean to gather relevant data for the purpose of modeling the user’s behavior
turns out to be a reasonable trade-off between intrusiveness, user acceptance
and quality of the data.
In typical AAL scenarios, vast amount of temporal data is generated through
the interaction of humans with the sensors onboard the nodes of the deployed
WSN. In this context, the interest in the adoption of Machine Learning method-
ologies to discover relevant patterns from streams of sensorial information is
constantly increasing [34]. In particular, the class of Recurrent Neural Networks
(RNNs) [33, 29] are recognized for their remarkable ability to effectively approach
learning tasks characterized by a distinct sequential/temporal nature in presence
of noisy and imprecise data, and it is therefore considered as particularly appro-
priate to approach the difficulties of the learning problems occurring in the AAL
applicative domains [37]. The Reservoir Computing (RC) [40, 35] paradigm and
the Echo State Network (ESN) [31, 30] model represent a theoretically grounded
methodology [17] for efficiently modeling and train RNNs. Characterized by a
huge popularity in many applicative domains involving temporal data process-
ing (see e.g. [36, 40]), the ESN approach has recently gained a great success
in real-world AAL-related tasks. Examples of relevant applications in this con-
text include indoor user context localization [9, 11, 5, 21], robot localization [14,
12], adaptive planning in personalized robotic applications [8], human gesture
recognition [20], human activity recognition [37, 2] and health care monitoring
for medical applications [7, 23, 3]. In addition to this, ESNs have been adopted
as core learning methodology in recent European initiatives, such as the FP7
RUBICON1 (Robotic UBIquitous COgnitive Network) project [1, 13], and the
FP7 DOREMI2 (Decrease of cOgnitive decline, malnutRition and sedEntariness
by elderly empowerment in lifestyle Management and social Inclusion) project
[38, 6]. Moreover, the ESN approach has also recently been investigated in per-
spective of the realization of a learning service for the Internet of Things [4]
and promises to greatly help in addressing the challenges involved by interaction
between robotic devices and the IoT, in the so-called Internet of Robotic Things
[39] framework.
Recently, with the introduction of the DeepESN model in [27, 18], the study
of hierarchically organized RC architectures is arousing an increasing interest.
Keeping the extreme efficiency of training algorithms as in standard RC net-
works, DeepESNs are capable of developing progressively more abstract repre-
sentations of temporal information in the levels of the architecture, potentially
allowing to naturally capture the structure of sequential data featured by mul-
tiple time-scales.
In this paper we investigate the introduction of the DeepESN approach in
learning tasks from streams of sensorial data in the AAL domain. In particular,
we provide an experimental analysis on two representative real-world benchmark
datasets concerning the identification of user behavior in indoor environment
based on data gathered from a small WSN. Specifically, a first dataset regards
the prediction of user’s spatial context in typical office environments, while a
second dataset targets a problem of Human Activity Recognition (HAR). In
both the cases the analysis is conducted in comparison to standard shallow
1
EU FP7 RUBICON project (contract no. 269914), http://fp7rubicon.eu/
2
EU FP7 DOREMI project (contract no. 611650), http://www.doremi-fp7.eu/
ESNs, in order to assess the impact of the proposed approach in this specific
applicative context.
The rest of this paper is organized as follows. In Section 2 we describe the
major characterizations of the DeepESN model. The results of the experimental
assessment on the two real-world benchmark datasets are provided and discussed
in Section 3. Finally, Section 4 concludes the paper.

2 Deep Echo State Networks

Within the randomized neural networks framework [16], the RC paradigm [40,
35] and the ESN model [31, 30, 17] represent the state-of-the-art RNN method-
ology for efficiently learn in temporal domains. ESNs (and in general all types
of RC networks) are based on a conceptual and practical distinction between a
dynamical component, called reservoir, and a feed-forward readout tool, which
is the only trained part of the network’s architecture. The reservoir implements
a randomized (temporal) filter which has the role of embedding the history of
input signals received by the network into a state space representation that pro-
vides a contextual memory to the system for each new input. The idea is that
if such temporal embedding is rich enough, then the representation of the prob-
lem at hand in the state space is likely to be solved by a trained linear readout
tool, and adaptation of the dynamical reservoir is not necessary. The well known
characterization of training efficiency of the RC approach naturally stems from
such consideration.
Recently, the ESN approach has been extended in the direction of deep learn-
ing with the introduction of the DeepESN model [27, 26, 18], in which the dy-
namical reservoir part of the network architecture is hierarchically organized into
layers. The study of DeepESNs has a twofold objective: on the one hand it aims
at the development of efficiently trained deep neural network models for learn-
ing in temporal domains, and on the other hand, by putting aside the aspects
related to learning of the recurrent connections, it allows to highlight and stress
the intrinsic properties of deep recurrent architectures. The analysis conducted
so far has shown that the layered composition of RNN layers is indeed able to
develop structured and rich representations of temporal information featured
by multiple time-scales dynamics [27, 18], as also pointed out through investiga-
tions on the short-term memory abilities [15], as well as by means of theoretical
studies in the field of dynamical systems [26] and Lyapunov exponents [24, 25].
Under an even broader perspective, the study of DeepESNs also allows to open
up interesting discussions about the true nature of deep learning for temporal
data processing [28]. On the application side, DeepESNs proved effective in both
synthetic and real-world cases, outperforming state-of-the-art results on several
versions of the multiple superimposed oscillator task [28] and, recently, showing
a very good predictive performance in a medical task related to diagnosis of
Parkinson’s disease [22]. Additional details and an up-to-date overview on the
advancements in the study of DeepESN can be found in [19].
The reservoir architecture of a DeepESN is composed of NL layers of recur-
rent units, where here we assume that each layer has the same size, denoted by
NR . Figure 1 gives a graphical illustration of the hierarchical reservoir organiza-
tion in a DeepESN. As it can be seen, the DeepESN state computation follows
the order in the architectural composition such that the first reservoir layer is
fed by the external input, while each successive layer receives in input the output
of the previous one.

1st layer 2nd layer NL -th layer

Fig. 1. Layered reservoir architecture of a DeepESN.

From a dynamical system point of view the reservoir of a DeepESN realizes
a discrete-time non-linear dynamical system that is driven by the external input
signal. More in detail, denoting by x(i) (t) ∈ RNR the reservoir state of layer i
at time step t, we can represent the global state of the whole network at time t
by x(t) = (x(1) (t), x(2) (t), . . . , x(NL ) (t)) ∈ RNL NR , which varies within a global
state space that is the product of all the individual reservoir spaces. Under this
viewpoint, the entire architecture of the stacked reservoirs is a dynamical system
that is ruled by global state transition function F , which expresses how the new
state of the network depends on the current input and on its previous state.
Such global function F can be decomposed into its layer-wise components, i.e.
F = (F (1) , F (2) , . . . , F (NL ) ), where each F (i) , for i = 1, 2, . . . , NL , denotes the
state transition function implemented at layer i.
In the following, we shall refer to the case of leaky integrator reservoir units
[32] and we will omit the bias terms in equations for the ease of presentation.
Denoting by u(t) ∈ RNU the external input at time step t, the state transition
function of the first reservoir layer, i.e. F (1) , computes the state of the first
reservoir layer as follows:

x(1) (t) = F (1) (u(t), x(1) (t − 1))
(1)
= (1 − a(1) )x(1) (t − 1) + a(1) tanh(Win u(t) + Ŵ(1) x(1) (t − 1)),

where, a(1) ∈ [0, 1] is the leaking rate parameter of the first layer, Win ∈
RNR ×NU denotes the input weight matrix, Ŵ(1) ∈ RNR ×NR is the recurrent
reservoir weight matrix for the first layer and tanh denotes the element-wise
applied hyperbolic tangent activation function. Following the pipeline on the hi-
erarchical reservoir architecture, each successive layer i > 1 is fed by the output
of the previous layer at the same time step. Thereby, the state of the reservoir
layer i at time step t is computed by the state transition function F (i) as follows:

x(i) (t) = F (i) (x(i−1) (t), x(i) (t − 1))
(i) (2)
= (1 − a(i) )x(i) (t − 1) + a(i) tanh(Wil x(i−1) (t) + Ŵ(i) x(i) (t − 1)),

(i)
where a(i) ∈ [0, 1] is the leaking rate parameter of the i-th layer, Wil ∈ RNR ×NR
is the inter-layer reservoir weight matrix for layer i and Ŵ(i) ∈ RNR ×NR is the
recurrent reservoir weight matrix for layer i. Note that in the above presented
mathematical description of the model, whenever the reservoir comprises only
one layer (i.e. for NL = 1), then a standard shallow ESN is obtained.
As in the standard RC framework, the reservoir part is left untrained after
being initialized under the conditions prescribed by the Echo State Property
(ESP) [30, 41], which has been extended to the case of deep RC architectures in
[26]. Typically, the reservoir is initialized according to the necessary condition
for the ESP, which expresses a stability constraint on the developed network
dynamics. Denoting by ρ(·) the spectral radius3 of its matrix argument, the
necessary condition for the ESP [26] requires that:

max ρ((1 − a(i) )I + a(i) Ŵ(i) ) < 1, (3)
i=1,2,...,NL

where I is the identity matrix of size NR × NR . Thereby, the initialization pro-
cess of a DeepESN consists in a random initialization (e.g. from a uniform dis-
tribution in [−1, 1]) of the reservoir in each layer, followed by a re-scaling to
ensure that the condition in equation 3 is satisfied. Moreover, matrices Win
(2) (N )
and Wil , . . . , Wil L are randomly initialized (e.g. from a uniform distribu-
tion in [−1, 1]) and then re-scaled to a desired value of their 2-norms, where
(i) (i)
sin = kWin k2 act as input scaling parameter and sil = kWil k2 is the inter-
layer scaling parameter of the i-th layer.
The output of the DeepESN is computed by the readout tool by means of
a linear combination of the reservoir states in the hierarchy. Although different
alternatives are possible, here we consider the case in which the input for the
readout layer is composed by the the global state of the network. Accordingly,
at time step t the output of the DeepESN, denoted by y(t) ∈ RNY is computed
as follows:
y(t) = Wout x(t), (4)
where Wout ∈ RNY ×NL NR is the readout weight matrix, which is adjusted on a
training set, typically by direct methods such as Moore-Penrose pseudo-inversion
or ridge regression [35].
For the purposes of our experimental analysis, we can distinguish two cases of
learning tasks on temporal data. Specifically, in the case of sequence-to-sequence
tasks an output element is required in correspondence of each input element,
and equation 4 is applied to every time step of the computation. In the case
of sequence-to-element tasks, the output is required only in correspondence of
3
The maximum among the eigenvalues in modulus.
the last element of the input sequence, and equation 4 is applied only to the
last time step (i.e. for each input sequence in correspondence of the last global
state of the DeepESN). In case of binary classification tasks, the output of the
network is discretized in {−1, 1} by applying the sign function to the output in
equation 4, whereas in case of multi-class classification tasks the output class
label is typically obtained by identifying the readout unit with the maximum
activation.
Further details on the DeepESN model can be found in recent literature [27, 26].

3 Experiments
In this section we present the results of the experimental assessment of Deep-
ESNs on two real-world benchmark datasets in the context of AAL applications.
Specifically, the adopted datasets are described in Section 3.1, while the experi-
mental settings and results achieved by DeepESNs are comparatively discussed
in Section 3.2.

3.1 Datasets
In our experiments we took into consideration two real-world datasets related to
the identification of human behavior in indoor environments. Both the datasets
have been designed and developed within the activities and collaborations of
our research group 4 as benchmarks in the perspective of being adopted for
evaluation purposes of methods to be used in AAL domains. To this aim, both
the datasets have been made freely available for download on the prominent and
well-known UCI Machine Learning Repository5 . As regards the specific aims
of this paper, the considered datasets are used as useful benchmarks for the
assessment of the impact of deep RNN architectures for time-series processing
on tasks from sensorial data.
The major features of the adopted datasets are summarized in the following.
Indoor Movement Forecasting. The first dataset that we take into consider-
ation pertains to scenario of anticipating user movements in a real-world indoor
office environment [9]. The prototypical environmental setting that is taken into
consideration consists in a couple of rooms separated by a corridor. The user is
walking in one of the two rooms and the goal is to anticipate whether she/he will
change room or not, once arrived in a marker position (symmetrically placed in
both the rooms). A WSN is placed in the environment, comprising 5 IRIS nodes,
4 of which act as anchors fixed nearby the corners of the rooms and the last one
is a mobile worn by the user. The input data consists in the 4-dimensional
stream of Received Signal Strength (RSS), sampled at the frequency of 8 Hz,
exchanged between the mobile and the anchors during the user’s movements un-
til the marker position is reached. The corresponding learning task is modeled
4
Computational Intelligence & Machine Learning (CIML) group, Department of Com-
puter Science, University of Pisa. Website http://www.di.unipi.it/groups/ciml/
5
https://archive.ics.uci.edu/ml/index.php
as a sequence-to-element binary classification task, in which the target output
associated to each input sequence is +1 for sequences that will lead to a room
change, and -1 for those leading to a room preservation. The dataset contains
information pertaining to 3 couples of rooms, gathered in a real-world setting
as described in [9], for a total number of 314 sequences. Data pertaining to
each couple of rooms is re-scaled in the range [−1, 1], individually for each RSS
trace. Figure 2 shows examples of noisy input signals corresponding to both the
cases of room change and room preservation. The Indoor Movement Forecasting
dataset is freely available at the address https://archive.ics.uci.edu/ml/
datasets/Indoor+User+Movement+Prediction+from+RSS+data.
Human Activity Recognition. The second dataset considered in our exper-
imental analysis is related to the recognition of human activities from RSS
data [37]. The goal is to recognize the action performed by the user within a
set of 7 daily-life activities, i.e. bending with legs straight, bending with legs
folded, cycling, lying, sitting, standing and walking. Sensor information is gath-
ered from a small WSN composed by 3 IRIS nodes, worn by the user and placed
on the chest, on the left ankle and on the right ankle. Input data is obtained from
the time series of RSS information exchanged among the 3 sensors, where the
average and the standard deviation of each of the 3 RSS traces was computed
over a time slot of 250 milliseconds. The dataset thereby comprises 6-dimensional
input sequences, with a sampling frequency of 4 Hz, corresponding to the RSS-
based setting of the dataset presented in [37]. The corresponding learning task
is modeled as a sequence-to-sequence multi-class classification task, in which
the (ground-truth) target output at each time step is represented as a (+1/-1)
1-of-7 encoding of the activity that the user is correspondingly performing. In
our experimental analysis, in order to obtain signals in a similar range of values
for each input dimension, we re-scaled the RSS averages by a factor of 100 and
the RSS standard deviations by a factor of 10. Examples of the re-scaled in-
put signals in correspondence of some of the activities considered in the Human
Activity Recognition dataset are illustrated in Figure 3, showing the high level
of noise in the involved time-series and the difficulty of recognizing clear pat-
terns by visual inspection. The Human Activity Recognition dataset comprises a

(a) (b)
1 1
RSS1
Input Signal

0.5 RSS2 0.5 RSS1
RSS3 RSS2
0 RSS4
0
RSS3

-0.5 -0.5 RSS4

-1 -1
0 20 40 60 0 10 20 30 40
Time step (8 Hz) Time step (8 Hz)

Fig. 2. Examples of RSS traces from the Indoor Movement Forecasting dataset. Input
signals correspond to: (a) room change (target class +1); (b): room preservation (target
class -1).
total number of 88 sequences, and can be freely downloaded at the following ad-
dress: https://archive.ics.uci.edu/ml/datasets/Activity+Recognition+
system+based+on+Multisensor+data+fusion+(AReM).

(a) (b)
1 1
RSSav1 RSSav1
RSSstd1 RSSstd1
RSSav2 RSSav2
Input Signal

RSSstd2 RSSstd2
RSSav3 RSSav3
RSSstd3 RSSstd3
0.5 0.5

0 0
0 20 40 60 80 100 120 0 20 40 60 80 100 120
(c) (d)
1 1
RSSav1 RSSav1
RSSstd1 RSSstd1
RSSav2 RSSav2
Input Signal

RSSstd2 RSSstd2
RSSav3 RSSav3
RSSstd3 RSSstd3
0.5 0.5

0 0
0 20 40 60 80 100 120 0 20 40 60 80 100 120
(e) (f)
1 1
RSSav1 RSSav1
RSSstd1 RSSstd1
Input Signal

RSSav2 RSSav2
RSSstd2 RSSstd2
RSSav3 RSSav3
0.5 RSSstd3 0.5 RSSstd3

0 0
0 20 40 60 80 100 120 0 20 40 60 80 100 120
Time step (4 Hz) Time step (4 Hz)

Fig. 3. Examples of input traces (100 time steps-long excerpts) from the Human Activ-
ity Recognition dataset. Input signals correspond to: (a): bending (with legs straight),
(b): cycling, (c): lying, (d): sitting, (e): standing, (f ): walking.

3.2 Results

In our experiments, we considered DeepESNs with NL = 10 layers, each of
which consisted in a fully-connected reservoir with NR = 10 units, resulting
in a total amount of 100 reservoir units. Note that, as pointed out also in [9],
such a choice allowed us to focus our analysis on a reservoir size that practically
allows the RC networks embedding into the nodes of a WSN. Moreover, we
assumed that the same values of the spectral radius, inter-layer scaling and
leaking rate parameters are adopted in each reservoir layer, i.e. for each i =
(i)
1, 2, . . . , NL we set ρ(i) = ρ, sil = sil (for i > 1) and a(i) = a. As regards the
readout training, we used ridge regression with regularization parameter denoted
by λr . For each task, we performed a model selection procedure to chose the
values of the RC hyper-parameters on a validation set, varying their values in
the ranges reported in Table 1, and according to the cross-validation schemes
described in the following. To this end, for each reservoir hyper-parameterization
we independently generated 20 networks guesses (with different random seeds),
and averaged the achieved results on such guesses.

Hyper-parameter Range of values
spectral radius ρ 0.7, 0.8, 0.9, 1
input scaling sin 0.1, 0.5, 1, 2, 5
inter-layer scaling sil 0.1, 0.5, 1, 2, 5
leaking rate a 0.1, 0.3, 0.5, 0.7, 1
readout regularization λr 10−9 , 10−8 , . . . , 10−2 , 10−1

Table 1. Range of RC hyper-parameters values considered for model selection.

The predictive performance on the Indoor Movement Forecasting task was
assessed in terms of (2 class) accuracy, while in the case of the Human Activity
Recognition task we evaluated the models performance in terms of their 7-class
accuracy, i.e. the rate of samples that are correctly assigned to their target class
label among the 7 possibilities.
As regards the model selection scheme, for the Indoor Movement Prediction
task we considered a setting close to the heterogeneous case in [9]. Specifically,
data pertaining to the first two couples of rooms were used as training set,
while data corresponding to the third couple of rooms represented an external
test set. Training data was then split into 5 folds, for a stratified nested 5-
fold cross-validation scheme designed for the purpose of model selection (on the
validation set). For the case of the Human Activity Recognition task, we adopted
a stratified 3-fold cross-validation, with a further internal level of stratified 4-
folds cross-validation used for model selection. In this regard, it is also worth
noticing that the model selection schemes adopted in this paper represent an
even more rigorous evaluation assessment procedure than those adopted in the
reference works for both the AAL tasks considered (see [9] and [37]).
For the sake of performance comparison, we ran experiments on the two AAL
tasks also with standard shallow ESNs, following the same settings adopted for
DeepESNs as described above. In particular, to the aim of comparison, it is
important to stress that we used ESNs with the same number of total recur-
rent units (and hence trainable parameters) as in the case of DeepESNs, with
100 units organized in a non-layered fully-connected reservoir architecture. This
allowed us to effectively and directly assess (by comparison) the effect of the hi-
model Training Validation Test
shallow ESN 0.97(±0.00) 0.91(±0.03) 0.84(±0.04)
DeepESN 0.98(±0.01) 0.95(±0.02) 0.90(±0.03)

Table 2. Training, validation and test accuracy achieved by DeepESN and shallow
ESN on the Indoor Movement task.

model Training Validation Test
shallow ESN 0.81(±0.01) 0.75(±0.02) 0.74(±0.01)
DeepESN 0.86(±0.01) 0.77(±0.03) 0.77(±0.02)

Table 3. Training, validation and test 7-class accuracy achieved by DeepESN and
shallow ESN on the Human Activity Recognition task.

erarchical organization of DeepESN reservoir state dynamics on the AAL tasks
under a fair condition on the number of free parameters of the learner.
The averaged predictive performance achieved by DeepESNs and shallow
ESNs on the Indoor Movement Forecasting task is reported in Table 2 (averaged
results and standard deviations are computed on the different reservoir guesses).
Results show that, in presence of a similar performance on the training set,
DeepESN achieves a higher accuracy than shallow ESN on both validation and
test sets, reaching respectively 95% and 90% of accuracy. This suggests that,
compared to standard shallow ESNs, DeepESNs on the one hand are able to
achieve higher accuracy under homogeneous environmental conditions (in which
the model is trained and assessed on data coming from the same set of envi-
ronments) and on the other hand they can better generalize to environmental
conditions completely unseen at the training stage.
Averaged results obtained by DeepESNs and shallow ESNs on the Human
Activity Recognition task are reported in Table 3 (averages and standard de-
viations are computed on the different reservoir guesses). As it can be seen,
DeepESNs outperform shallow ESNs, being able to better fit the training data,
and at the same time reaching a higher performance on the validation and test
sets, on both of which it is obtained a 7-class accuracy of 77%.
The results obtained by DeepESNs on both the AAL tasks are relevant also
in relation to those reported in literature on the same tasks. In particular, as
regards the Indoor Movement Prediction task, the test performance of Deep-
ESN reported in this paper is higher than those reported in [9] in the closest
experimental setting6 , even with respect to 5 times larger RC networks. Fur-
thermore, the 7-class accuracy on the test set reported here for DeepESN on
the Human Activity Recognition task is also comparable with the performance
reported in [37] under the closest experimental setting7 , although the latter has
been achieved with possibly much larger RC networks and in correspondence
6
Non-local 4 setting of the heterogeneous task, as reported in [9].
7
RSS-based setting of the activity recognition system, as reported in [37].
of a less thorough scheme for performance assessment than the one considered
here8 .
Overall, the results of the experimental analysis provided in this section
clearly point out the convenience of a hierarchical organization of the recur-
rent dynamical part of the neural network architecture in learning tasks from
streams of sensorial data within the AAL domain. From a practical perspective,
our results showed that given a recurrent network architecture with a limited
size of 100 units (which realistically allows a direct embedding into a node of a
WSN, as discussed also in [9]), and given the training efficiency characterization
common of the RC framework, the layered DeepESN organization is preferable
to a standard shallow one.

4 Conclusions

In this paper we have proposed an experimental investigation aimed at assess-
ing the introduction of the DeepESN methodology for applications in the area
of AAL from temporal data originated by a network of sensors. To this aim we
have conducted experiments on two real-world benchmark datasets related to the
identification of human indoor behavior from temporal streams of RSS informa-
tion. On both the considered tasks DeepESNs were able to overcome the results
achieved by standard ESNs under the same experimental settings and number of
trainable parameters. Limiting our analysis to a total number of reservoir units
that represent a suitable case for embedding into small devices, DeepESNs led
to increase of classification accuracy, with respect to shallow ESNs, of 6% on the
Indoor Movement Forecasting (binary classification) task, and of 3% (in terms of
7-class accuracy) on the Human Activity Recognition (multi-classification) task.
The analysis proposed in this paper pointed out the performance advantage
brought about by a layered RNN architecture in dealing with AAL tasks: given
the same amount of recurrent units it is indeed a good idea to stack them into a
layered network when dealing with tasks in this domain. Moreover, the very good
result achieved by DeepESNs in the analyzed cases also provide an interesting
insight on the appropriateness of the structured feature representations devel-
oped by hierarchical reservoirs when excited by temporal data gathered from
sensors. This would in turn indicate the relevance, at least partial, of a mul-
tiple time-scales nature of the temporal data involved in this application field.
Overall, the experimental assessment presented in this paper indicates that the
DeepESN approach, yet inheriting the desirable RC characterization of training
efficiency, is able to further enhance the already good predictive ability of RC
networks, and it is therefore put forward as an effective methodology for learning
in temporal domains for future real-world AAL applications.
8
Results reported in [37] have been obtained with reservoirs up to 500 units and
considering an hold-out cross-validation scheme, such that the test set performance
therein reported refers to a smaller set of samples than the one considered in this
paper.
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