Transfer Learning Algorithms for Autonomous
Reconfiguration of Wearable Systems
Ramyar Saeedi, Hassan Ghasemzadeh and Assefaw H. Gebremedhin
School of Electrical Engineering and Computer Science
Washington State University
Emails: {rsaeedi, hassan, assefaw}@eecs.wsu.edu
Abstract—Wearables have emerged as a revolutionary technology in many application domains including healthcare and fitness.
Machine learning algorithms, which form the core intelligenceof wearables, traditionally deduce a computational model froma set of training examples to detect events of interest (e.g.
activity type). However, in the dynamic environment in which
wearables typically operate in, the accuracy of a computationalmodel drops whenever changes in configuration of the system(such as device type and sensor orientation) occur. Therefore,
there is a need to develop systems which can adapt to the new
configuration autonomously. In this paper, using transfer learning
as an organizing principle, we develop several algorithms for data
mapping. The data mapping algorithms employ effective signal
similarity methods and are used to adapt the system to the new
configuration. We demonstrate the efficacy of the data mapping
algorithms using a publicly available dataset on human activity
recognition.

I.

I NTRODUCTION

Wearables have received tremendous attention due to theirpotential to fundamentally transform applications in healthcare,wellness, fitness, etc. The growing ubiquity of sensor-equipped
wearables such as mobile devises, pedometers, EEGs (electroencephalogram), and smartphones has made it possible tocapture information about human behavior in real-time. This
growth is leading to increased development and deploymentof mobile sensing applications and products [1]–[6].
Wearables need to cope with data streams of high heterogeneity and volume that the transmission, storage, organization, and, most importantly, analysis and interpretation of thedata call for tasks that can rightly be categorized as “big data”
problems [7], [8]. As an example, consider a wearable inertial
sensor which collects tri-axial accelerometer, gyroscope andmagnetometer data at 50 Hz. Assuming a collection rate of two
bytes per sample per signal, this will generate approximately
75 MB of data per day for just one user.
Despite their enormous potential, however, currently existing wearables are designed for controlled environments, lab
settings, and small trials with configuration-specific protocols.
Scaling these systems up and extending their applications inreal-world environments brings about major challenges.
Machine learning and signal processing algorithms form
the core intelligence of wearables. Traditional machinelearning-based approaches used in contexts involving wearables suffer from several limitations, however. First, the accuracy of computational algorithms drops whenever the system
specification or installation changes (e.g. sensor calibration,
device orientation, sampling frequency). Second, model retraining can be expensive and time consuming. In particular,
computational algorithms for such tasks as classification andtemplate matching typically need training data to constructprocessing models for that specific configuration. Retraining
the computational algorithms for every configuration requires
collecting sufficient amount of labeled training data, a processthat is known to be time consuming, labor-intensive, andexpensive. Third, data collected for one setting (e.g. a specific
type or brand of sensor) may no longer be suitable for a
new setting. For example, the training data may be collectedusing shimmer sensors while the final data processing may be
conducted on a different platform such as smart-phones andsmart-watches.
In this paper, we aim to address these limitations andmake progress towards developing robust and efficient methods
for autonomous reconfiguration of wearable systems. Next
generation wearables need to be computationally autonomousin the sense that their underlying computational algorithms
reconfigure automatically without the need for collecting newlabeled training data.
Within this broad aim, the focus of our current work is on
design of algorithms for mapping sensor data from one setting
to another related but different setting. We approach the data
mapping problem from a transfer learning perspective. We
show that the data mapping can be used a) to avoid dependence
on labeled training data from the source domain and b)
to integrate different datasets of the same sensor type. As
accelerometers are among the most common sensors [9] andare widely used in human physical monitoring systems (e.g.
activity recognition and gait analysis), we use accelerometerdata to assess the efficacy of our algorithms. We outline ourcontributions as follows.
a) Algorithms. We have developed three different algorithms for data mapping. All three algorithms are used for thecommon purpose of extracting knowledge from the training
data. The first two algorithms are based exclusively on signal
similarity while the third is based on signal motifs. We study
the three algorithms and present the trade-off each offers.
b) Evaluation. Using a dataset on human activity recognition system, we show that signal variation can decrease activity
recognition performance by as much as 25%. Signal variations
include different subjects, data sampling frequency, and sensor
calibration. Also, we assess the performance of the three data
mapping algorithms and their effects on the activity recognition
performance. We show that the accuracy of the data mapping
algorithms is high (R-squared = 0.59 to 0.79). We also showthat it improves the accuracy of the activity recognition system
by up to 15%.
The remainder of the paper is organized as follows. In
Section II, we provide necessary background on transfer

In particular, transductive transfer learning corresponds to thecase where Ds 6= Dt and Ts = Tt . In Section II-B, weintroduce measures that are used in this paper to extract
knowledge from signals collected using wearables.
B. Signal Processing
Figure 1: Traditional machine learning vs transfer learning.

learning and signal processing. In Section III, we present thethree data mapping algorithms we developed. In Section IV,
we describe the human activity dataset, and we discuss theeffects of signal variation on activity recognition. We also
experimentally evaluate the performance of the proposed data
mapping algorithms. We discuss related work in Section V,
and close with concluding remarks in Section VI.
II.

BACKGROUND

A. Transfer Learning
A common assumption in machine learning algorithms
is that the training and testing data are in the same feature
space with the same distribution [10]. However, in real-world
applications, we may have different feature spaces and/or
distributions. When faced with a new situation, recognizing
and applying relevant knowledge gained in a related earlier
situation is clearly profitable—humans do this rather routinely.
Intuitively, transfer learning follows a similar idea. It aims at
developing methods to transfer knowledge gained in a sourcetask to improve learning in a related target task. We refer tothe training domain where labeled data is available as thesource domain, and to the test domain where labeled data
is unavailable or very little is available as the target domain[11]. Fig. 1 illustrates the difference between transfer learningand traditional machine learning. We formally define transfer
learning as follows.
Definition 1 (Transfer Learning): Given a source domain
Ds and learning task Ts , and a target domain Dt and learning
task Tt , transfer learning aims to improve the accuracy of thetarget predictive model Mt in Dt using the knowledge in Dt
and Ds in the case where Ds 6= Dt and/or Ts 6= Tt .
In transfer learning, there are three main questions toanswer: (1) what to transfer; (2) how to transfer; (3) whento transfer [10], [12]. To find answers to these questions,
we need to extract application-specific metrics to measure
the quality of available knowledge. Furthermore, we need todevelop algorithms to transfer the knowledge in a way that
increases the performance of the target domain. Finally, weneed to notify the system whenever it is necessary to transfer
the available knowledge.
In this paper, transfer learning algorithms are developed
for the purpose of autonomous reconfiguration of wearables.
Our assumption is that we have a computational model fora specific application of wearables. However, the accuracy ofcomputational model drops dramatically for the same application in the presence of signal heterogeneity. This problem
falls into a subcategory called transductive transfer learning.

Wearables collect raw data from sensors constantly. The
raw data includes long time-series data which will be used inboth the training and testing phases of a specific application.
A time series is a sequence T = (t1 , t2 , . . . , tl ), where eachti is a real number. To extract meaningful information fromthe sensor data, we need to segment the time series into
subsequences. Each subsequence corresponds to a data point(e.g., a specific activity type).
There are many subsequences in a given source domain.
Of these, we need to identify those subsequences that are best
related to the target domain. To identify such subsequences weneed signal similarity measures to quantify the relatedness.
There exist different similarity measures in the literature,including cross-correlation and dynamic time warping (DTW).
In this work, we use cross-correlation, which we formallydefine below.
Definition 2 (Cross Correlation): Given two time series
T1 and T2 , the cross-correlation between T1 and T2 is a vector
where each entry is the inner-product of T1 and a given lag(shift) of T2 .
The motivation for considering different lags of T2 is tosearch for the best possible alignment between the two timeseries. Typically, the maximum value in the cross-correlation
vector is reported as the cross-correlation value for the twotime series. For better interpretability, the time series are firstnormalized in such a way that the autocorrelation at zero lag
is equal to one. The result is referred to as normalized crosscorrelation (NCC). NCC always lies between −1 and 1.
The large size of incoming data for applications of wearables (e.g. activity recognition) and having noisy signals due
to inherent inaccuracy of sensors inspired researchers to design
algorithms for detecting repeatedly occurring subsequences intime series [9], [13]. These subsequences are called signal
motifs and are widely used in time series data mining problems.
Below is a formal definition of signal motifs.
Definition 3 (Signal Motif): Given a time series T oflength l, an integer p  l, and a predefined distance threshold
dth , a motif of length p is the subsequence that appears mostfrequently in the time series T .
Sometimes motifs other than the most frequent one arealso of interest. In general, the subsequence that appears thek th most frequent time in the time series is called k-motif.
With this denotation, motif and 1-motif are synonyms.
Time series data mining algorithms have high runtime complexity, and similarity search methods in such algorithms canbecome bottlenecks for classification. Signal motifs increase
the scalability in terms of time and memory as there is noneed to search or save the entire data. Signal motifs are in a
way a “signature“ of the signal source. Therefore, motif-based
signal processing is well-suited for wearables with limited
computation and communication capability [9].

Algorithm 1 F IND PATTERN

Algorithm 2 Brute Force ADM Algorithm (BFADM)

Input: T , pat, ol
Output: match, sim
sim = 0, start = 1, stop = pat.size
while stop ≤ T.size do
max = SIMILARITY(T (start : stop), pat)if max > sim thensim = max
match = T (start : stop)
end ifstart = stop − ol + 1
stop = start + pat.size − 1
end while

Input: T = {Tseg1 , . . . , Tsegn }, DBS , ol, λOutput: MappingFunction F
Initialize P airs ← ∅for all Tsegi ∈ T dofor all DBSi ∈ DBS do(sim, match) ← F IND PATTERN(Tsegi , DBSi , ol)
if sim > λ then
P airs ← P airs ∪ (match, Tsegi )end ifend forend forF ← M APPING M ODEL(P airs)

III.

DATA M APPING A LGORITHMS

We present in this section the three different asynchronous
data mapping (ADM) algorithms we developed to support
knowledge transfer in wearable systems. We use the term
asynchronous to indicate that the data in the source domain
and target domain can be collected at different times. Before
presenting the data mapping algorithms, we introduce a procedure that is used by all three algorithms and whose purpose is
to find a specified pattern in a given time series. This pattern
finding algorithm is outlined in Algorithm 1. The algorithm
takes as input a time series T , a pattern pat, and an integer olindicating the overlap between consecutive windows allowedin the search for pat in T . The value of ol ranges between zeroand the size of the pattern (pat.size). The algorithm returns thesubsequence match in T that is the most similar to pat, andthe numerical value of the similarity, sim, which is returnedby the subroutine SIMLARITY.
A key component in all three of the data mapping algorithms is the knowledge from the source domain(s), which
we call dictionary. Below, we formally define dictionary,following similar notation as in [14].
Definition 4 (Dictionary): A dictionary (database) DB isan unordered set of time series of possibly different lengths.
We denote by DBi the ith time series in the dictionary, andby DBij the j th sample from DBi . Each time series DBi canbe a signal motif, a data segment, or a time series. Also, eachtime series can have multiple dimensions.
The dictionary is an important construct as the knowledge
stored in the dictionary determines the time needed for searching relevant knowledge and the amount of memory needed tostore the knowledge.
A. The Brute Force ADM Algorithm (BFADM)
Our first data mapping algorithm, outlined in Algorithm 2,
is a brute force method. The algorithm takes as input atime series T = {Tseg1 , . . . , Tsegn }, the dictionary DBs of
the source domain, the overlap allowed ol, and a similarity
threshold λ. For each Tsegi , Algorithm 2 calls Algorithm 1
to find the closest data segment to Tsegi in the dictionary
DBs , and adds it to the data structure P airs. This is repeateduntil all the selected segments in the target domain are treated(outer for-loop). The structure P airs obtained at the end of
the iteration is used to build the mapping model. The mapping
model is returned as the output.

The brute force search employed in Algorithm 2 is accurate, but it can be inefficient in terms of runtime. In the next
two subsections, we present two other algorithms which are
more efficient in terms of runtime, while their accuracy is lessbut still acceptable.
B. The Clustering-based ADM Algorithm (CADM)
Our second algorithm aims to reduce the search space using
a clustering approach. To find signal segments that can begrouped together, we first build a network of signal segments,
and then apply a community detection method on the resulting
network to obtain the clustering. We formalize the approach
below.
Definition 5 (Similarity Network): Given the source data
segments S = {Sseg1 , . . . , Ssegn }, the similarity network G =(V, E) is a network where each vertex vi ∈ V correspnds todata segment Ssegi , and (vi , vj ) ∈ E whenever the Euclidean
distance dist(i, j) between data segments Ssegi and Ssegj islower than a pre-specified threshold dt .
After the similarity network is clustered, we randomly
select several vertices from each community as representativesof their clusters. These representatives are used to find the pairsneeded for building the mapping model. Algorithm 3 outlines
the clustering-based ADM algorithm.
As discussed earlier, the clustering approach reduces thecost of search for similar pairs, but this comes at the expenseof the time spent on building and clustering the similarity
network. However, the clustering can be performed offline,
making the approach a better choice for real-time execution.
C. The Motif-based ADM Algorithm (MADM)
The idea behind our third data mapping algorithm is to
reduce the size of the dictionary in order to make the searchfor pairs faster. Here, using motif discovery techniques, signal
motifs are extracted for every time series in the source domain
dictionary DBS . The algorithm, outlined in Algorithm 4, takes
as input the time series T from the target domain, the source
domain dictionary DBS , a similarity threshold λ, the minimum
distance for motif discovery d, and the number k of motifs ineach time series. Like the previous two algorithms, its output
is a mapping model.
There exists number of work on locating known patterns
in time series [13]–[15]. In Algorithm 4, we extract k motifs

Algorithm 3 Clustering-based ADM Algorithm (CADM)
Input: S = {Sseg1 ,. . . , Ssegn }, T = {Tseg1 ,. . . , Tsegm }, λ, dt
Output: Mapping Function F
Initialize P airs ← ∅Build G(V ← {v1 , v2 , . . . , vn }, E ← ∅)for i = 1 to n do
for j = 1 to n dosim ← SIMILARITY(Ssegi , Ssegj )if sim > dt thenE ← E ∪ (vi , vj )end ifend forend forC ← C OMMUNITY D ETECTION(G), where C is Clusters
Let c be a representative from C
for all Tsegi ∈ T do
DBC ← C LOSEST C OMMUNITY(c ∈ C, Tsegi )(sim, match) ← F IND PATTERN(Tsegi , DBC )if sim > λ then
P airs ← P airs ∪ (match, Tsegi )end ifend forF ← M APPING M ODEL(P airs)
Algorithm 4 Motif-based ADM Algorithm (MADM)
Input: T = {Tseg1 , . . . , Tsegn }, DBS , λ, k, d
Output: Mapping Function F
Initialize P airs ← ∅, DM ← ∅for all DBSi ∈ DBS do
DM ← DM ∪ F IND M OTIFS(DBSi , k, d)
end forfor all Tsegi ∈ T do
Initialize max ← 0.
for all DMj ∈ DM do(sim, match) ← F IND PATTERN(DMj , Tsegi )if sim > λ then
P airs ← P airs ∪ (match, Tsegi )end ifend forend forF ← M APPING M ODEL(P airs)for each activity type, and save all of the motifs in a dictionary
named DM . The motif discovery phase would be off-line anddoesn’t increase real-time execution. We used the brute force
algorithm proposed by Abdullah et. al. [14] to find the motifs.
We modified the algorithm using a minimum distance toconsider a pattern as signal motif. The function F IND M OTIFS
returns the k-motifs for a given distance threshold d and agiven time series DBS i . Then, for each Tsegi , Algorithm 1 iscalled to find the closest data segment to Tsegi in the dictionary
DM , which then is added to the data structure P airs. Thisis repeated until we have enough number of pairs to build themapping model.
IV.

E XPERIMENTAL E VALUATION

We experimentally assess the efficacy of our approach
using a publicly available dataset at University of California,

Irvine (UCI) Machine Learning Repository [16]. We implemented all of the algorithms we developed and all activity
recognition related processing in MATLAB.
In the remainder of this section, we (a) describe the dataset,(b) discuss the the experimental setup for all three algorithms
and present results for the motif discovery, network-based
clustering, and comparison of data mapping algorithms, and (c)conduct a case study in which we evaluate activity recognition
performance with and without data mapping.
A. Dataset Description
The dataset we used represents different kinds of signal
heterogeneity, including signal sampling frequency, sensor
calibration, and subject-related factors [16]. Nine subjects
were asked to perform six different daily activities: Walking,Sitting, Standing, Stair down, Stair up, and Biking. Subjects
were asked to perform the activity set while carrying eightsmartphones—two instances of four device types—on theirwaist. Each smartphone is equipped with tri-axial accelerometer. The maximum sampling frequency for the different devices
is given in the table below:
LG Nexus 4
200 HzGalaxy S+
150 HzGalaxy S3
100 HzGalaxy S3 mini
50 Hz

Each subject conducted five minutes of each activity, which
ensured equality of available data for all activities.
B. Data Mapping Algorithms Evaluation
Since the data we collected is sensor data from 3-axis
accelerometer, the data is 3-dimensional. For the simulation,
we set λ = 0.8 for all three dimensions. In other words, twodata segments are considered similar, whenever the NCC for all
axis is at least 0.8. A sliding window of two seconds was used
for data segmentation. Furthermore, we apply interpolation
techniques on the data whenever the source and the target
domain time series sampling frequencies are different. Below,
we provide results on motif discovery and clustering that will
be used in the data mapping algorithms. We also provide theruntime comparison for the three data mapping algorithms.
1) Network-based Clustering Results: We built the similarity networks for each smartphone separately. Whenever
the source domain is a specific smartphone, we use thecorresponding similarity network. We normalized all the data
segments before similarity check. We set dt = 3.7 to build
the similarity network. For similarity network, we only usedthe data of Walking, Stair up, Stair down, and Biking. The
reason for not using Standing and Sitting is because these two
activities are standstill.
For community detection, we used the multi-level community detection algorithm implemented in the iGraph software package [17]. We chose the algorithms because of its
low computational complexity. Table I shows the number ofclusters obtained when the community detection is appliedon the similarity network for two smartphones (LG Nexus
and S3 mini). The average, minimum, and maximum distancevalues (between pairs of vertices) in a cluster are presented as
way to gauge the quality of clustering. Each of these values
is the average for all clusters in the network. The average
Euclidean distance observed (1.09 and 1.22) show that theTable I: Network-based clustering results.
Device/Parameter
LG Nexus
S3 mini# Vertices
2136
3342

# Clusters
6
5

Min dist
0.22
0.19

Avg. dist
1.09
1.22

Max dist
1.80
1.95

domain.
Table II shows the R-squared value of the regression modelfor the various algorithms. In the table, the source domain isaccelerometer data of the first LG Nexus smartphone and thetarget domain is the activity data of the second LG Nexus
smartphone.
C. Case Study: Activity Recognition
Figure 2: Normalized execution time comparison for the threealgorithms for both real-time and off-line data processing for
the Samsung S3 mini smartphone.

data points in each cluster are highly similar compare to theaverage distance of 3.2 for the entire network. In addition, thenumbers of clusters is also close to the number of activities(recall that there are 4 activities). We point out that we usedthe clustering approach to also make the data “cleaner”. In
particular, we removed the small clusters that consisted of only
a few vertices (in total this made up less than 2% of the data).
These small clusters contain outliers and noisy data segments,
which includes nearly 10% of data segments for each device.
2) Motif Discovery: The brute force motif discovery
method in the MADM algorithm uses Euclidean distance
between two data segments in the motif discovery process[14]. The minimum distance d that we considered for motif
discovery is 0.05. For each subject and smartphone, we haveten to fifteen motifs discovered from each activity. Using the
motif discovery approach the size of dictionary decreases by up
to two orders of magnitude. If we considered each smartphone
as a source domain, the size of the dictionary in each domain is
at most 60 time series which is significantly smaller comparedto the case where all time series are stored in the dictionary.
3) Execution Time Comparison: Fig. 2 shows the normalized runtime of different algorithms for the Samsung S3mini smartphone. As we can see, for real-time execution, theMADM algorithm is the fastest since it has the list dictionary
size among the three algorithms. On the other hand, the offline processing time is higher for MADM, as we need to run
motif detection to construct the dictionary.
4) Data Mapping Accuracy: As was discussed in Section III, we used the set of signal segment pairs that werecomputed within each of the three algorithms to build the
M APPING M ODEL. For the M APPING M ODEL, we used linear
regression model based on the Pairs of data segments. To build
the regression model, two different approaches were used: a)
1-axis regression model: for each axis of accelerometer inthe target domain, the regression model is built based on thecorresponding axis in the source domain. b) 3-axes regression
model: for each axis of accelerometer in the target domain, theregression model is built based on all three axes in the source

To evaluate the performance of the data mapping algorithms, we built activity recognition models. We apply amoving average filter on the activity data to remove highfrequency noises from acceleration data. Therefore, anotherexternal factor is eliminated from the acceleration data. Afterdata filtering, a two-second sliding window is used for data
segmentation. We consider the overlap as 25% of a window for
activity recognition. A variety of features can be extracted fromhuman activity data segments. Previous studies have identified
statistical features that are effective for activity recognition[18], [19].
We used different types of classifiers, including decision
tree, random forest, and k-nearest neighbor (k-NN) algorithms.
We considered cross-validation as the baseline for our comparison. For the baseline, the training data consists of samples
from all subjects and devices. In other words, we have label
data for all configurations. We consider three scenarios as
described below to show the performance of the data mapping
built based on the various algorithms.
Inter-Subject: the computational model is built based on
all the subjects except one which is used for test. We onlyused one of the LG Nexus smartphones data. Therefore, thesource domain is data from the eight subjects, and the target
domain is the excluded subject. We used all three algorithms
to build a data mapping function to map the target domain
to the source domain. Fig. 3a shows the average of activity
recognition for cross-validation, BFADM, CADM, MADM,
and without transfer learning (unmapped data). The transfer
learning approach increases the accuracy by up to 6%.
Inter-Device: for Samsung Galaxy S3, we use one of theinstances for training and the other for testing. Therefore, thesource domain is data from the one device, and the target
domain is the other device from the same model. Fig. 3bshows the average of activity recognition for cross-validation,
BFADM, CADM, MADM, and without transfer learning. The
transfer learning approach increases the accuracy from 4% to
13% for different scenarios.
Inter-Model: we build the classifiers using the data of
one specific device type and test it using the other devices.
Hence, the source domain is data of a specific model, andtarget domain is the data of other models. Fig. 3c showsthat our approach outperforms the traditional machine learning
approach by up to 13%. We used the Samsung S3 devices as
the source and on LG Nexus smartphone as the target domain.

Table II: Comparison of the three algorithms BFADM, CADM and MADM in terms of R-squared value for two different data
mapping regression models (1-axis and 3-axis).

1-axis3-axes

X
0.68
0.73

(a) Subject-related heterogeneity

BFADM
Y
Z
0.71 0.68
0.76 0.71

X
0.63
0.73

CADM
Y
Z
0.67 0.70
0.74 0.69(b) Device-related heterogeneity

X
0.61
0.69

MADM
Y
Z
0.67 0.59
0.71 0.73(c) Sampling frequency heterogeneity

Figure 3: Comparison of activity recognition accuracy with and without transfer learning algorithms for different types of signal
heterogeneity.

V.

R ELATED W ORK

The robustness of prediction model for wearables is widely
studied in the literature [20]–[28]. In [21], a set of heuristics ispresented for activity recognition with sensors displaced within
a single body segment. The authors show that within certain
limits and with modest quality degradation, activity recognition
is tolerant to displacement. Forster et. al. [22] proposed a
genetic programming-based feature selection algorithm. The
goal is to find a set of discriminative and variation tolerant
features that also reduce the energy requirements of the system.
The results showed an average 80% of activity recognition
accuracy. In [23], the authors present an on-body sensor
localization for 7 different locations in wearable sensors. It
is assumed that there are activity recognition models foreach location which needs to collect training data for eachmodel. Allan et. al. [16] investigate sensor heterogeneity inactivity recognition systems. A clustering based method is
proposed to categorize different devices and then perform
activity recognition using a specific computational model foreach category. This approach needs to collect training data foreach device category. Zappi et. al. [24] investigate the effects
of exploiting redundant sensors distributed on the body to use asubset of sensors in the presence of disorientation. They show
that, although the individual classification accuracy remains
low, the classification accuracy can be improved to 80% with
3 sensors and up to 98% with all 57.
In the dynamic environment of wearables, the assumptionthat different configurations are available is unrealistic. Therefore, there is need to develop transfer learning approaches
for wearables. Here, we review some recent work related toour problem. Sandeep et. al. [25] propose a feature subset
selection method for domain adaptation, where the criterion
for selecting features is to minimize an approximated distancefunction between the distributions in the two domains. Kyleet. al. [26] proposed three heterogeneous transfer learningtechniques that reverse the typical transfer model and map thetarget feature space to the source feature space and apply themto activity recognition in a smart apartment. In another work[27], a multi-view learning approach is presented for wearables
with multiple sensors in which one of them acts as the source
domain. It is shown that by using an additional wearable node
the performance can increase by up to 15%. Alinia et. al. [28]proposed a reconfigurable framework for estimation of MET
in wearables with limited number of sensor locations.
VI.

C ONCLUSION

We proposed three algorithms to support autonomous reconfiguration of wearable systems under dynamic environments. Whenever there is a change in configuration of system(e.g. sensor calibration), mapping algorithms are build basedon the available knowledge from the source domain(s) toadapt the computational model of the target domain. This
reconfiguration phase may run on the back-end server or on
the wearable system locally.
The data mapping algorithms are developed based on similarity measures and motif discovery techniques. This mapping
functions are useful in two different ways. First, the systems
performance is preserved in case of signal heterogeneity. Secondly, there is no need to collect data for the new configuration.
We focused on activity recognition as our case study
and performed analysis using 3-axis accelerometer data. We
focused on heterogeneity of sensor prototypes, sampling frequency and, subject specific variations. Based on our casestudy, we make the following observations:•

Sampling frequency heterogeneity affects the accuracyof activity recognition more than sensor calibrationand subject-related heterogeneity do.

•

Cross-correlation is a poor measure to capture subjectrelated heterogeneity.

•A prediction model built using a device with highsampling frequency gives the least performance if thetest device sampling frequency is low (Fig. 3c).

Based on our experimental evaluation, we find that the performance boost our framework yields ranges between 3% to 13%
compared to the case where a reconfiguration phase is absent
from the system. Furthermore, we find that the three data
mapping algorithms we developed offer a trade-off between
computational complexity and performance.

[11]

[12]

[13]

[14]

We plan to extend this work in several directions. First,
we are working on integrating additional sensor modalities
such as gyroscopes into our wearable system. Second, we planon building algorithms for more complex heterogeneity suchas mis-orientation and misplacement of sensors. Specifically,
we plan to build knowledge transfer algorithms using better
network similarity-based methods. Third, we plan to explore
the suitability of other similarity metrics than cross-correlation,
including dynamic time warping (DTW).

[15]ACKNOWLEDGMENTS[18]

This work was supported by a Research Fellowship fromthe Community Health Analytics Initiative (CHAI) at Washington State University and by the National Science Foundation CAREER Award IIS-1553528.

[19]

R EFERENCES
[1]

[2]

[3]

[4]

[5]

[6]

[7]

[8]

[9]

[10]

H. Cai and K. K. Venkatasubramanian, “Detecting malicious morphological alterations of ecg signals in body sensor networks,” in Proceedings of the 14th International Conference on Information Processing in
Sensor Networks. ACM, 2015, pp. 342–343.
M. A. Brodie, M. J. Coppens, S. R. Lord, N. H. Lovell, Y. J. Gschwind,
S. J. Redmond, M. B. Del Rosario, K. Wang, D. L. Sturnieks, M. Persiani et al., “Wearable pendant device monitoring using new waveletbased methods shows daily life and laboratory gaits are different,”
Medical & Biological Engineering & Computing, vol. 54, no. 4, pp.
663–674, 2016.
J. Lecoutere, A. Thielens, S. Agneessens, H. Rogier, W. Joseph, andR. Puers, “Wireless fidelity electromagnetic field exposure monitoringwith wearable body sensor networks,” IEEE transactions on biomedical
circuits and systems, vol. 10, no. 3, pp. 779–786, 2016.
P. E. Eggers, E. A. Eggers, and B. Z. Bailey, “Wearable apparatus, system and method for detection of cardiac arrest and alerting emergency
response,” Jun. 23 2016, uS Patent 20,160,174,857.
R. Fallahzadeh, M. Pedram, R. Saeedi, B. Sadeghil, M. Ongl, and
H. Ghasemzadeh, “Smartcuff: A wearable biosensing platform withactivity-sensitive information quality assessment for monitoring ankle
edema,” 2015.
C. Simon, C. Cain, S. Hajiammini, R. Saeedi, M. Schmitter-Edgecombe,and D. Cook, “Digital memory notebook: experimental evaluation of
motivational reward strategies,” in Proceedings of the 2014 ACM International Joint Conference on Pervasive and Ubiquitous Computing:
Adjunct Publication. ACM, 2014, pp. 1201–1208.
D. Zakim and M. Schwab, “Data collection as a barrier to personalized
medicine,” Trends in pharmacological sciences, vol. 36, no. 2, pp. 68–
71, 2015.
H. Banaee, M. U. Ahmed, and A. Loutfi, “Data mining for wearable
sensors in health monitoring systems: a review of recent trends andchallenges,” Sensors, vol. 13, no. 12, pp. 17 472–17 500, 2013.
A. Vahdatpour, N. Amini, and M. Sarrafzadeh, “Toward unsupervised
activity discovery using multi-dimensional motif detection in timeseries.” in IJCAI, vol. 9, 2009, pp. 1261–1266.
S. J. Pan and Q. Yang, “A survey on transfer learning,” IEEE Transactions on knowledge and data engineering, vol. 22, no. 10, pp. 1345–
1359, 2010.

[16]

[17]

[20]

[21]

[22]

[23]

[24]

[25]

[26]

[27]

[28]

J. Jiang, “A literature survey on domain adaptation of statistical classifiers,” URL: http://sifaka. cs. uiuc. edu/jiang4/domainadaptation/survey,
2008.
L. Torrey and J. Shavlik, “Transfer learning,” Handbook of Researchon Machine Learning Applications and Trends: Algorithms, Methods,
and Techniques, vol. 1, p. 242, 2009.
B. Liu, J. Li, C. Chen, W. Tan, Q. Chen, and M. Zhou, “Efficient motif
discovery for large-scale time series in healthcare,” IEEE Transactions
on Industrial Informatics, vol. 11, no. 3, pp. 583–590, 2015.
A. Mueen, E. Keogh, Q. Zhu, S. Cash, and B. Westover, Exact
Discovery of Time Series Motifs, ch. 40, pp. 473–484.
B. Chiu, E. Keogh, and S. Lonardi, “Probabilistic discovery of timeseries motifs,” in Proceedings of the ninth ACM SIGKDD international
conference on Knowledge discovery and data mining. ACM, 2003,
pp. 493–498.
A. Stisen, H. Blunck, S. Bhattacharya, T. S. Prentow, M. B. Kjærgaard,
A. Dey, T. Sonne, and M. M. Jensen, “Smart devices are different:
Assessing and mitigatingmobile sensing heterogeneities for activity
recognition,” in Proceedings of the 13th ACM Conference on Embedded
Networked Sensor Systems. ACM, 2015, pp. 127–140.
G. Csardi and T. Nepusz, “The igraph software package for complex
network research,” InterJournal, Complex Systems, vol. 1695, no. 5, pp.
1–9, 2006.
H. Ghasemzadeh, E. Guenterberg, and R. Jafari, “Energy-Efficient
Information-Driven Coverage for Physical Movement Monitoring in
Body Sensor Networks,” IEEE Journal on Selected Areas in Communications, vol. 27, pp. 58–69, 2009.
R. Saeedi, B. Schimert, and H. Ghasemzadeh, “Cost-sensitive feature
selection for on-body sensor localization,” in Proceedings of the 2014
ACM International Joint Conference on Pervasive and Ubiquitous
Computing: Adjunct Publication. ACM, 2014, pp. 833–842.
R. Saeedi, R. Fallahzadeh, P. Alinia, and H. Ghasemzadeh, “An energyefficient computational model for uncertainty management in dynamically changing networked wearables,” in International Symposium on
Low Power Electronics and Design (ISLPED). IEEE/ACM, 2016.
K. Kunze and P. Lukowicz, “Using acceleration signatures from everyday activities for on-body device location,” in Wearable Computers,
2007 11th IEEE International Symposium on. IEEE, 2007, pp. 115–
116.
K. Forster, P. Brem, D. Roggen, and G. Troster, “Evolving discriminative features robust to sensor displacement for activity recognition inbody area sensor networks,” in Intelligent Sensors, Sensor Networks andInformation Processing (ISSNIP), 2009 5th International Conference
on. IEEE, 2009, pp. 43–48.
R. Saeedi, N. Amini, and H. Ghasemzadeh, “Patient-centric on-bodysensor localization in smart health systems,” in Signals, Systems andComputers, 2014 48th Asilomar Conference on. IEEE, 2014, pp. 2081–
2085.
P. Zappi, T. Stiefmeier, E. Farella, D. Roggen, L. Benini, and G. Troster,“Activity recognition from on-body sensors by classifier fusion: sensor
scalability and robustness,” in Intelligent Sensors, Sensor Networks
and Information, 2007. ISSNIP 2007. 3rd International Conference on.
IEEE, 2007, pp. 281–286.
S. Satpal and S. Sarawagi, “Domain adaptation of conditional probability models via feature subsetting,” in European Conference on
Principles of Data Mining and Knowledge Discovery. Springer, 2007,
pp. 224–235.
K. Dillon Feuz and D. J. Cook, “Heterogeneous transfer learning foractivity recognition using heuristic search techniques,” International
Journal of Pervasive Computing and Communications, vol. 10, no. 4,
pp. 393–418, 2014.
S. A. Rokni and H. Ghasemzadeh, “Plug-n-learn: automatic learning of computational algorithms in human-centered internet-of-things
applications,” in Proceedings of the 53rd Annual Design Automation
Conference. ACM, 2016, p. 139.
P. Alinia, R. Saeedi, R. Fallahzadeh, A. Rokni, and H. Ghasemzadeh,“A reliable and self-adaptive signal processing framework to estimate
metabolic equivalent of task in wearable sensors,” IEEE Journal of
Selected Topics in Signal Processing, no. 99, pp. 1–13, 2016.