The MobiAct Dataset: Recognition of Activities of Daily Living using

Smartphones

George Vavoulas

, Charikleia Chatzaki

1,2

, Thodoris Malliotakis

, Matthew Pediaditis

1,2

and Manolis Tsiknakis

1,2

Technological Educational Institute of Crete, Department of Informatics Engineering,

Biomedical Informatics and eHealth Laboratory, Estavromenos, 71004, Heraklion, Crete, Greece

Foundation for Research and Technology – Hellas, Institute of Computer Science,

Computational BioMedicine Laboratory, Vassilika Vouton, 71110, Heraklion, Crete, Greece

Keywords: Human Activity Recognition, Activities of Daily Living, Smartphone, Accelerometer, Dataset.

Abstract: The use of smartphones for human activity recognition has become popular due to the wide adoption of

smartphones and their rich sensing features. This article introduces a benchmark dataset, the MobiAct

dataset, for smartphone-based human activity recognition. It comprises data recorded from the

accelerometer, gyroscope and orientation sensors of a smartphone for fifty subjects performing nine

different types of Activities of Daily Living (ADLs) and fifty-four subjects simulating four different types

of falls. This dataset is used to elaborate an optimized feature selection and classification scheme for the

recognition of ADLs, using the accelerometer recordings. Special emphasis was placed on the selection of

the most effective features from feature sets already validated in previously published studies. An important

qualitative part of this investigation is the implementation of a comparative study for evaluating the

proposed optimal feature set using both the MobiAct dataset and another popular dataset in the domain. The

results obtained show a higher classification accuracy than previous reported studies, which exceeds 99%

for the involved ADLs.

1 INTRODUCTION

Human activity recognition is the process of

identifying and recognizing the activities and goals

of one or more humans from an observed series of

actions. In recent years, human activity recognition

has evoked notable scientific interest due to its

frequent use in surveillance, home health

monitoring, human-computer interaction, ubiquitous

health care, as well as in proactive computing.

Human activities can be further decomposed as a set

of basic and complex activities, namely activities of

daily living (ADLs) and instrumental activities of

daily living (IADLs). Typical approaches use vision

sensors, inertial sensors and a combination of both.

Exploiting the increasing tendency of smartphone

users, latest reports introduce systems which use

smartphone sensors to recognize human activities

(Kwapisz et al., 2011; Siirtola and Röning 2012;

Khan et al., 2010; Lee and Cho, 2011).

The aim of this work is to introduce a benchmark

dataset and to present an optimized system in terms

of feature selection and classification for recognition

of ADLs based on smartphone's triaxial

accelerometer data. The “MobiAct” dataset contains

records of the accelerometer, gyroscope and

orientation sensors of a smartphone from fifty

subjects performing nine different types of ADLs

and fifty-four subjects performing four different

types of falls. In order to achieve an optimized

recognition system, special emphasis was placed on

the selection of the most effective features from

feature sets already validated in published studies.

Furthermore, a comparison study was performed to

evaluate the proposed optimal feature set with the

MobiAct dataset, as well as with an additional

dataset. The results show higher classification

accuracy than previous reported studies.

2 RELATED WORK

As already said, human activity recognition has

evoked notable scientific interest in recent years. A

recent study (Bayat et al., 2014) proposes a

smartphone-based recognition system, in which the

Vavoulas, G., Chatzaki, C., Malliotakis, T., Pediaditis, M. and Tsiknakis, M.

The MobiAct Dataset: Recognition of Activities of Daily Living using Smar tphones.

In Proceedings of the International Conference on Information and Communication Technologies for Ageing Well and e-Health (ICT4AWE 2016), pages 143-151

ISBN: 978-989-758-180-9

143

application of a low-pass filter and a combination of

Multilayer Perceptron, LogitBoost and Support

Vector Machine (SVM) classifiers reached an

overall accuracy of 91.15% when the smartphone

was held in the hand of the user. Samples were

recorded from four volunteers while performing six

activities: slow and fast running, walking, aerobic

dance, ascending stairs (“stairs up”) and descending

stairs (“stairs down”). The sampling rate was set at

100 Hz while a window of 1.28 seconds with 50%

overlap was used for feature extraction.

Anjum and Ilyas (2013) introduced a similar

approach with ten users performing seven different

activities which included walking, running, stairs up,

stairs down, cycling, driving and remaining inactive,

by carrying the smartphone in various positions. A

sampling rate of 15 Hz and matching time windows

of 5 seconds were used. Based on the ranking of the

information gain, nine features were selected from

the auto correlation function. For the classification

process Naϊve Bayes, C4.5 Decision Tree, K-Nearest

Neighbor and SVM classifiers were tested. The C4.5

Decision Tree performed better than the other

classifiers with an accuracy of 95.2%.

Zheng et al. (2014) proposed a two-phase method

to achieve recognition of four different types of

activities (sitting, standing, walking and running)

using tri-axial acceleration data from a Samsung

galaxy SIII smartphone. Five subjects performed the

activities with the phone placed loosely in a pocket.

Records of two minutes were used for the training

phase while for the testing phase data from

continuous records of several days were used. A

sampling rate of 100 Hz was used. In order to achieve

noise reduction, the authors deployed Independent

Components Analysis, specifically the fastICA

algorithm, in combination with the wavelet transform

for feature extraction. For the classification, a Support

Vector Machine was employed using the WEKA

toolkit. A maximum accuracy of 98.78% was

reported for a leave-one-out validation.

Based on tri-axial accelerometer data of a

smartphone, Buber and Guvensan (2014) developed

a recognition system for the following activities:

walking, jogging, jumping, stairs up, stairs down,

sitting, standing and biking. Five volunteers

performed those activities with the smartphone

placed in the front pocket of their trousers. The

sampling rate was set at 20 Hz and a 10 second

moving window was used for feature extraction. The

evaluation was performed with two feature selection

algorithms (OneRAttributeEval and ReliefF

AttributeEval) and six classification algorithms (J48,

K-Star, Bayes Net, Naïve Bayes, Random Forest,

and k-NN) using 10-fold cross-validation. The

authors resulted in a combination of 15 features with

k-NN to perform best at a recognition rate of 94%.

Fan et al. (2013) studied three different decision

tree models based on a) the activity performed by the

user and the position of the smartphone (vector), b)

only the position and c) only the activity. Fifteen users

performed five kinds of activities: stationary, walking,

running, stairs up and stairs down with the smartphone

placed into a carrying bag, a trouser pocket or in the

hand. Ten-second samples of accelerometer data were

recorded for each different kind of activity and position

of smartphone. The authors concluded that the model

based only on the activity outperformed the other two

with an accuracy of 88.32%.

In another study (Siirtola and Roning, 2013),

accelerometer data from a smartphone were

recorded with a sampling frequency of 40Hz while

seven volunteers were performing five different

activities: walking, running, cycling, driving a car,

and sitting/standing. In each recording, four

smartphones were placed in various positions,

namely, trousers’ front pocket, jacket’s pocket, at

backpack, at brachium and one was held at the ear

only when it was physically allowed. For feature

extraction a sliding window of 7.5 seconds with 25%

overlap in an online (on device) application and one

with 50% overlap in an offline application, were

used. Classification was achieved using five

classifiers based on quadratic discriminant analysis

arranged in a three stage decision tree topology.

Average recognition rate of almost 98.9% was

reported in the offline and 90% in the online system.

Exploiting the accelerometer sensor of a

smartphone (Dernbach et al. 2012) developed a

system for recognizing simple (biking, stairs up,

driving, lying, running, sitting, standing and

walking) and complex (cooking, cleaning etc.)

activities performed by ten participants. The

sampling frequency was set at 80 Hz maximum

although variations in the sampling rate were

reported. Multiple windows sizes of 1, 2, 4, 8 and 16

seconds with 50% overlap were used. The placement

of the smartphone, in terms of position and

orientation, was left at each user’s will. Although

complex activities were classified with an accuracy

of 50%, simple activities were classified with 93%

accuracy with a Multilayer Perceptron and a window

size of 2 seconds.

Saputri et al. (2014) proposed a system for

activity recognition in which twenty-seven subjects

performed six types of activities, namely, walking,

jogging, running, stairs up, stairs down and hopping.

The smartphone was placed in the front trouser

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Table 1: Overview of the methodology and results followed by the related studies.

Study

No of

subjects

Activities

Sampling

Frequency

Window

size/overlap

No of

Features

Smartphone

position

Algorithms

Performance

(Bayat et

al., 2014)

RUN, SWL

FWL,

ADN, STU,

STN

100Hz 1.28s/50% 18

hand of the

user

J48, K-

Star, BN,

NB, RF,

kNN

MLP & LB

&SVM:

91,15%

Accuracy

(Anjum

and Ilyas,

2013)

RUN, STN,

STU, BIK,

STC, DRI,

INA

15Hz 5s 9*

various

positions

NB, C4.5,

KNN,

SVM

C4.5:

95.2%.

(Zheng et

al., 2014)

SIT, STD,

WAL, RUN

100Hz -

ICA +

Wavelet

freely in

pocket

SVM 98.78%

(Buber

and

Guvensan

, 2014)

WAL,

JOG, STN,

STU, SIT,

JUM, BIK

20Hz 10s 15

front

pocket

J48, K-

Star, BN,

NB, RF,

kNN

k-NN: 94%

(Fan et

al., 2013)

STC,

WAL,

RUN,

STU,STN

- 10s 10*

bag,

trouser

pocket &

hands

ID3 DC 80.29%

(Siirtola

and

Roning,

2013)

WAL,

RUN, BIK,

DRI,

SIT/STD

40Hz

7.5s/25%

online app

7.5s/50%

offline app

smartphone

s : various

position

DC &

QDA

90% online

98.9%

offline

(Dernbac

h et al.,

2012)

BIK, STU,

DRI, LAY,

RUN, SIT,

STD WAL.

80Hz

1,2,4,8,16/50

user’s

choice

(position &

orientation)

MLP, NB,

BN, DT,

B-FT, K-

star

MLP: 93%

2s window

(Saputri

et al.,

2014)

WAL, RUN,

STN,STU,

HOP

50Hz 2s 21

front

pocket

ANN 93%

WAL: Walking, JOG: Jogging, STN: Stairs down, STU: Stairs up, SIT: Sitting, STD: Standing, RUN: Running, BIK: Biking, LAY:

Laying down, STC: Static, ADN: Aerobic dancing, HOP: Hopping, DRI: Driving, INA: Inactivity.

J48: Weka implementation of C4.5 Decision Tree, LR: Logistic Regression, MLP: Multilayer Perceptron, kNN: k-Nearest Neighbors,

SMO: Sequential Minimal Optimization, NB: Naïve Bayes, SVM: Support Vector Machines, RF: Random Forest, DT: Decision Table, B-

FT: Best-First Tree

Feature set includes that number of features but is not limited to.

pocket using a sampling rate of 50 Hz. In the feature

extraction process, the window size was set at 2

seconds, while feature selection was performed

using a self-devised three-staged genetic algorithm.

The use of an Artificial Neural Network produced

93% accuracy in the activity recognition.

The above non-exhaustive review on ADLs

recognition systems using smartphone embedded

inertial sensors reveals that several research studies

have already been published, reporting acceptable

results while employing various different data

processing and analysis approaches. However, there

is an inherent weakness of conducting objective

comparisons between different implementations,

because of the heterogeneity of the acquired raw

data, as shown in Table 1. The issue of

differentiation in smartphone positions, sampling

frequency and the kinds of activities addressed,

along with the relatively small number of subject

recordings is addressed in the following work with

the use of the developed MobiAct dataset.

3 THE MOBIACT DATASET

3.1 Dataset Description

MobiAct is a publicly available dataset (available for

download from www.bmi.teicrete.gr) which includes

data from a smartphone when participants are

performing different types of activities and a range

of falls. It is based on the previously released

MobiFall dataset (Vavoulas et al. 2014), which was

initially created with fall detection in mind. The fact

that MobiFall included various activities of daily

living made it also suitable for research in human

activity recognition. In its current version, and with

The MobiAct Dataset: Recognition of Activities of Daily Living using Smartphones

145

a more generic name, MobiAct is introduced for the

first time in the context of this study.

It encompasses four different types of falls and

nine different ADLs from a total of 57 subjects with

more than 2500 trials, all captured with a smartphone.

The activities of daily living were selected based on

the following criteria: a) Activities which are fall-like

were firstly included. These include sequences where

the subject usually stays motionless at the end, in

different positions, such as sitting on a chair or

stepping in and out of a car; b) Activities which are

sudden or rapid and are similar to falls, like jumping

and jogging; c) The most common everyday activities

like walking, standing, ascending and descending

stairs (“stairs up” and “stairs down”). These activities

were included from the start of the effort, since our

ultimate objective has been to extend our work

towards recognition of not only falls, but also

complex everyday activities and, eventually,

behaviours. Moreover, the fact that such activities are

included is an advantage concerning human activity

recognition (HAR) in general. As a result, MobiAct is

suitable investigating both fall detection and HAR.

Table 2 and Table 3 summarize all captured activities

(and activity codes), their present trial counts,

durations and a short description for each activity.

3.2 Dataset Acquisition Details

All activities related to the design of the acquisition

protocol and the acquisition of the MobiAct dataset

itself were performed at the Technological

Educational Institute of Crete. Data were recorded

from the accelerometer, gyroscope and orientation

sensors of a Samsung Galaxy S3 smartphone with

the LSM330DLC inertial module (3D accelerometer

and gyroscope). The orientation sensor is software-

based and derives its data from the accelerometer

and the geomagnetic field sensor. The gyroscope

was calibrated prior to the recordings using the

device’s integrated tool. For the data acquisition, an

Android application has been developed for the

recording of raw data for the acceleration, the

angular velocity and orientation (Vavoulas et al.

2013). In order to achieve the highest sampling rate

possible the parameter “SENSOR_DELAY

FASTEST” was enabled. Finally, each sample was

stored along with its timestamp in nanoseconds.

The techniques applied in the majority of

published studies focusing on smartphone-based

activity recognition, require the smartphone to be

rigidly placed on the human body and with a specific

orientation. For this purpose a strap is frequently

used. In contrast to this and in an attempt to simulate

every-day usage of mobile phones, our device was

located in a trousers’ pocket freely chosen by the

subject in any random orientation. For the falls, the

subjects used the pocket on the opposite side of the

direction of the fall to protect the device from

damage. For the simulation of falls a relatively hard

mattress of 5 cm in thickness was employed to

dampen the fall (Vavoulas et al. 2014).

3.3 Dataset Participants

For the generation of the MobiAct dataset 57

subjects (42 men and 15 women) were recorded

while performing the predefined activities. The

subjects’ age spanned between 20 and 47 years

(average: 26), the height ranged from 160 cm to 189

cm (average: 175), and the weight varied from 50 kg

to 120 kg (average: 76). 50 subjects completed

Table 2: Falls recorded in the MobiAct dataset.

Code Activity Trials Duration Description

FOL Forward-lying 3 10s Fall Forward from standing, use of hands to dampen fall

FKL Front-knees-lying 3 10s Fall forward from standing, first impact on knees

SDL Sideward-lying 3 10s Fall sideward from standing, bending legs

BSC Back-sitting-chair 3 10s Fall backward while trying to sit on a chair

Table 3: Activities of Daily Living recorded in the MobiAct dataset.

Code Activity Trials Duration Description

STD Standing 1 5m Standing with subtle movements

WAL Walking 1 5m Normal walking

JOG Jogging 3 30s Jogging

JUM Jumping 3 30s Continuous jumping

STU Stairs up 6 10s Stairs up (10 stairs)

STN Stairs down 6 10s Stairs down (10 stairs)

SCH Sit chair 6 6s Sitting on a chair

CSI Car step in 6 6s Step in a car

CSO Car step out 6 6s Step out of a car

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successfully all ADLs and 54 subjects completed all

falls. In total 10 trials had to be removed from the

dataset due to errors in acquisition.

4 METHODS

4.1 Datasets for Comparison and

Evaluation

Our intention for generating MobiAct was to enable

testing and benchmarking between various methods

for human activity recognition with smartphones. As

a result a comparison to other existing and publically

available datasets is of significant value. The most

suitable such public dataset is the WISDM dataset

(Kwapisz et al., 2011). Both WISDM and MobiAct

datasets include a large set of the same ADLs,

namely walking, jogging, stairs up, stairs down,

sitting and standing, in a common file format.

Moreover, the position of the mobile device is

equally treated in both datasets since it is up to each

subject to freely select the orientation it will be

placed into the pocket.

Other freely available datasets, such as the

DALIAC dataset (Leutheuser et al., 2013) and the

UCI dataset (Anguita et al., 2012) could not be used

for comparison since they differ significantly in

terms of the recorded ADLs and the data acquisition

conditions, which should be overlapping as much as

possible among all the datasets under consideration.

For example, the DALIAC dataset uses multiple

accelerometer nodes statically placed on the human

body. It does not use smartphone-based inertial

sensors and therefore it is not suitable for the study

at hand. The UCI data were recorded with a specific

position for the smartphone (waist mounted). In

addition, the UCI dataset does not include the

jogging activity, which is part of both MobiAct and

WISDM datasets, but instead includes the lying

down activity, which is not part of MobiAct and

WISDM. Apart from these differences, significant

differences in the data format prevented the

utilization of the UCI dataset.

4.2 Pre-processing

In order to extract features from the two selected

datasets a common file format and sampling rate for

both must be achieved. Following MobiAct’s file

format, the WISDIM raw data file was split into

smaller files based on the subject’s ID and activity.

Linear interpolation and subsampling was applied on

the MobiAct data in order to achieve a 20Hz sampling

frequency which is what is used for the production of

the WISDM dataset. 20Hz as a sampling frequency is

also reported by Shoaib et al. (2015) as being suitable

for the recognition of ADLs from inertial sensors. In

MobiAct, the duration of some types of activities was

smaller than 10 sec, which is the time window for

feature extraction that the WISDM study uses

(Kwapisz et al., 2011). To achieve a minimum of 10

sec trial duration especially in trials of stairs up, stairs

down and sitting on chair the last sample of each file

in question was padded.

4.3 Reproduction of the WISDM Study

An important qualitative part of this investigation is

the validation of the feature extraction techniques

through the reproduction of a published

computational pipeline and the comparison of the

results. For this purpose the reported study (Kwapisz

et al., 2011) was selected, which uses the WISDM

dataset. Our hypothesis is that, if the results of the

reproduction of the WISDM study are

approximately the same as the published results,

then the feature set defined could be used for a

comparison to other feature sets, such as the one

reported by Vavoulas et al. (2014).

The results from the reproduction of the WISDM

study are presented in Table 4. In general the

reproduced and the reported results have the same

behaviour in both studies. Some minor deviations

may be due to slight differences in the windowing

and feature extraction methodology, since, as

previously mentioned, we had to split the WISDM

data into smaller files.

4.4 Feature Extraction & Feature Sets

In attempting to estimate with the parameters for an

optimal computational and analysis pipeline, it is

obvious that the selection of a respective optimal

feature set is of paramount importance. To construct

this feature set, a combination of features from the

study using the precursor of MobiAct (Vavoulas et

al. 2014) and the WISDM study (Kwapisz et al.,

2011) were used.

4.4.1 Feature Set A (FSA)

This feature set consists of 68 features based on the

reported work in (Vavoulas et al. 2014). For most of

the features a value was extracted for each of the

three axes (x, y, z). In detail, the following features

were computed within each time window:

The MobiAct Dataset: Recognition of Activities of Daily Living using Smartphones

147

 21 features in total from: Mean, median,

standard deviation, skew, kurtosis, minimum

and maximum of each axis (x, y, z) of the

acceleration.

 1 feature from: The slope SL defined as:

=



(



−



)



+(



−



)



+(



−



)



(1)

 4 features from: Mean, standard deviation,

skew and kurtosis of the tilt angle TA

between

the gravitational vector and the y-axis (ssince

the orientation of the smartphone was not

predefined it is expected that the negative y-

axis will not be always pointing towards the

vertical direction). The tilt angle is defined as:





=sin

















+





+







(2)

where x, y and z is the acceleration in the

respective axis.

 11 features from: Mean, standard deviation,

minimum, maximum, difference between

maximum and minimum, entropy of the energy

in 10 equal sized blocks, short time energy,

spectral centroid, spectral roll off, zero crossing

rate and spectral flux from the magnitude of the

acceleration vector.

 31 additional features were calculated from the

absolute signals of the accelerometer, including

mean, median, standard deviation, skew,

kurtosis, minimum, maximum and slope.

4.4.2 Feature Set B (FSB)

A total of 43 features were generated in accordance

to the WISDM study reported by Kwapisz, Weiss

and Moore (2011) as variants of six basic features.

For each of the three axes, the average acceleration,

standard deviation, average absolute difference, time

between peaks and binned distribution (x10 bins)

were calculated in addition to the average resultant

acceleration as a single feature.

4.4.3 Optimal Feature Set (OFS)

Following elaborate experimentation (totally 70

different experimental setting) in which a) various

combinations of window size (10, 5, 2 sec) and

overlap (0%, 50%, 80%) were tested, b) features

were removed or added into the feature vector based

on observations of the achieved accuracy, and c)

different classifiers were employed, such as IBk,

J48, Logistic regression, Multilayer Perceptron and

LMT (from the WEKA’s algorithm set), an optimal

feature set, in our view, has been produced. All

experiments were conducted using 10-fold cross-

validation. Specifically, the two feature sets (FSA

and FSB), obtained using a time window of 5 sec

and 80% overlap, were at first combined to form one

new feature set. Subsequently weak features,

identified through a trial-and-error approach, were

taken out in an iterative process until the best overall

accuracy for both datasets (MobiAct and WISDM)

was obtained. A total number of 64 features were

thus retained to form the optimal feature set. The

features excluded from FSA were kurtosis for the x,

y and z axes and spectral centroid. The features

excluded from FSB were: time between peaks,

binned distribution and average absolute difference.

The optimal feature set was also calculated by using

a 10s window and no overlap as defined in the

WISDM study for a final comparison to their results,

as shown in Table 4.

Table 4: Classification results (% accuracy) in comparison to the WISDM published results (10s window size, no overlap).

Activity

Published Results Reproduced Results (FSB)

Results using the optimal

feature set (OFS)

J48 Logistic

Multilayer

Perceptron

J48 Logistic

Multilayer

Perceptron

J48 Logistic

Multilayer

Perceptron

Walking 89.9 93.6 91.7 90.8 93.8 95.3 99.4 98.3 99.8

Jogging 96.5 98.0 98.3 98.5 98.6 99.0 99.1 99.4 99.6

Upstairs 59.3 27.5 61.5 65.5 53.2 79.3 85.2 79.5 92.5

Downstairs 55.5 12.3 44.3 55.6 49.7 69.4 87.4 77.4 91.5

Sitting 95.7 92.2 95.0 97.0 94.1 94.6 97.0 97.5 98.0

Standing 93.3 87.0 91.9 97.0 94.6 90.4 99.4 97.0 99.4

Overall 85.1 78.1 91.7 88.3 87.5 92.4 96.7 94.9 98.2

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Table 5: Classification results using the optimal feature set (5s window size, 80% overlap).

Dataset/Classifier: MobiAct/IBk MobiAct/J48 WISDM/IBk WISDM/J48

Activity TPRate FPRate TPRate FPRate TPRate FPRate TPRate FPRate

Walking 1.000 0.000 1.000 0.000 1.000 0.000 0.998 0.002

Jogging 1.000 0.000 1.000 0.000 0.999 0.000 0.998 0.001

Upstairs 0.993 0.001 0.930 0.004 0.992 0.001 0.939 0.006

Downstairs 0.982 0.000 0.921 0.003 0.991 0.001 0.937 0.007

Sitting 1.000 0.000 0.999 0.000 0.999 0.000 0.996 0.000

Standing 1.000 0.000 1.000 0.000 0.999 0.000 0.996 0.000

Accuracy: 99.88 % 99.30 % 99.79 % 98.63 %

4.5 Classifiers

The classifiers selected for the final testing of the

optimal feature set were the IBk (with 1 nearest

neighbor), the J48 decision tree, Logistic regression

and Multilayer perceptron, included in WEKA (Hall

et al. 2009) with default parameters. The first two

produced the best overall results, whilst the

remaining two were used for a comparison to the

WISDM study since they were also reported there.

5 RESULTS

The experimental results obtained using the optimal

feature set are shown in Table 5. It is worth noticing

that with both classifiers the overall accuracy is

close to 99% for both datasets. The best accuracy for

the MobiAct dataset is obtained with the IBk

classifier. IBk generally appears to have a relative

better performance with 94% accuracy, a fact that

has already been reported elsewhere (Buber and

Guvensan, 2014). Also, IBk performs better than J48

for the WISDM dataset as well. The weakness in

accurately recognizing activities which produce

similar signals, such as stairs up and stairs down, is

noticeable with J48. Nevertheless, IBk recognizes

these activities effectively. An additional noticeable

point is that IBk performs slightly better in

classifying the walking activity, which has been

observed to be often misclassified as a stairs up or

stairs down activity.

Considering the comparison of the results when

using FSB (reproduced results) and OFS with the

WISDM dataset, for all the classifiers used, OFS

outperforms FSB (Table 4). A possible explanation

to this may be the higher number of features used in

OFS. This finding is in line with related published

evidence. As reported by Siitrola and Roning (2013),

accuracy of 98.9% achieved with the use of a large

feature set (75 features).

6 CONCLUSIONS

The study’s objective was to estimate an optimal

computational and analysis pipeline which

accurately recognizes ADLs exploiting an extensive

dataset of motion data collected from a smartphone.

As a result of this investigation a set of 64 features

that proved to perform best with two datasets was

extracted. These features were the outcome of many

tests, through a trial and error process that removed

weak features such as kurtosis and spectral centroid.

It is noticeable that absolute values of kurtosis in all

three axes improve the performance of classification

and hence were included in the final optimal feature

set. The spectral centroid is the key feature, which

affects the results of activity recognition negatively.

The stairs up and stairs down activities exhibit the

worst accuracy among all those performed in the

tests. This observation is also seen in other reports

and may be related with the random device

orientation or the dynamic and temporal resolution

of the accelerometer sensor.

The best overall accuracy of 99.88% is achieved

when using the IBk classification algorithm on the

MobiAct dataset in combination with the optimal

feature set mentioned above. This is the best

reported classification result to date, when

comparing with the most recent studies presented in

Table 1. This result is the outcome of a 10-fold

cross-validation which is a very common evaluation

approach in the related studies, although we expect

to decrease when using a leave-one-out cross-

validation, which is a more realistic scenario. It is

the intention of the authors to advance into such

validation scenarios in the near future. For the above

results a sampling rate of 20Hz, a window size of 5

seconds and an overlap of 80% have been used.

These values are proposed as the optimal for this

experimental setup. The usage of two independent

datasets ensures robustness of the results, always

within the limits of each dataset.

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Finally, the experimental results obtained

indicate that the MobiAct can be considered as a

benchmark dataset since it includes a relatively large

number of records and a wide range of activities in

an easy to manage data format. Furthermore, since

the placement of the smartphone is freely chosen by

the subject in any random orientation we believe that

it represents real life conditions as close as possible.

The next step towards developing a real-life

application requires that a) orientation data is used in

a more efficient manner and b) assessment and

optimization of power consumption (battery usage)

requirements for the feature extraction and

classification algorithms, is thoroughly studied.

ACKNOWLEDGEMENTS

This work is partly funded by the WeMP – Wellness

Management Platform funded by FORTHnet S.A.

The authors gratefully thank all volunteers for

their contribution in the generation of the MobiAct

dataset.

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