Human Activity Recognition Based on Novel Accelerometry Features and

Hidden Markov Models Application

Ana Lu

ısa Gomes

, V

ıtor Paix

and Hugo Gamboa

1,2

Universidade Nova de Lisboa Faculdade de Ci

encias e Tecnologias, FCT-UNL, Lisbon, Portugal

PLUX - Wireless Biosignals, Lisbon, Portugal

Champalimaud Neuroscience Programme, Champalimaud Centre for the Unknown, Lisbon, Portugal

Keywords:

Human Activity Recognition, Forward Feature Selection, Hidden Markov Models, Clustering.

Abstract:

The Human Activity Recognition (HAR) systems require objective and reliable methods that can be used in

the daily routine and must offer consistent results according to the performed activities.

In this work, a framework for human activity recognition in accelerometry (ACC) based on our previous work

and with new features and techniques was developed. The new features set covered wavelets, the CUIDADO

features implementation and the Log Scale Power Bandwidth creation. The Hidden Markov Models were also

applied to the clustering output. The Forward Feature Selection chose the most suitable set from a 423

di-

mensional feature vector in order to improve the clustering performances and limit the computational demands.

K-means, Afﬁnity Propagation, DBSCAN and Ward were applied to ACC databases and showed promising

results in activity recognition: from 73.20% ± 7.98% to 89.05% ± 7.43% and from 70.75% ± 10.09% to

83.89% ± 13.65% with the Hungarian accuracy (HA) for the FCHA and PAMAP databases, respectively. The

Adjust Rand Index (ARI) was also applied as clustering evaluation method. The developed algorithm consti-

tutes a contribution for the development of reliable evaluation methods of movement disorders for diagnosis

and treatment applications.

1 INTRODUCTION

Over time, the increasingly demand for objectiv-

ity in clinical diagnosis and the continuous pur-

suit for human wellbeing led to the development

of enginery for healthcare. The combined efforts

of medicine and engineering created and developed

techniques that provide large amounts of informa-

tion and simultaneously allow to interpret the gen-

erated data. According to different studies and our

previous work, accelerometry is a reliable system for

monitoring and evaluate daily physical activities over

time(In

es Prata Machado, 2014), (Nishkam Ravi and

Littman, 2005), (A. M. Khan and Kim, 2010). In this

study, a framework for HAR systems was developed

and tested with different accelerometry databases ac-

quired with a triaxial accelerometer.

Biosignal processing requires an acquisition stage

and a transformation with conversion, ﬁltering and

extraction of the useful features, which will depend

on the aim of the investigation. The feature extraction

step becomes very important for activity recognition

because it deﬁnes what information we will cluster

with. The selected features are directly related with

the information extracted from the ACC signals which

allows data organization inside each cluster by clus-

tering algorithms. The clustering organization must

show a lower variation between similar activities than

between different activities (Lin and Chen, 2005),

(Nishkam Ravi and Littman, 2005).

1.1 Unsupervised Learning Methods

Several techniques for data acquisition and process-

ing have been developed to improve the early di-

agnosis and to aid clinical treatment of various dis-

eases. ACC signals processing shows the importance

of objective monitoring human locomotion through

movement quantiﬁcation when the medical diagno-

sis of pathologies is subjective and hard to trace such

as Parkinson’s disease and Cerebrovascular Accident

(CVA).

U. Maurer and coworkers (Uwe Maurer and

Deisher, 2006) and our group (In

es Prata Machado,

2014) concluded that four features from time and fre-

quency domains can achieve high activity recogni-

Luísa Gomes A., Paixão V. and Gamboa H..

Human Activity Recognition Based on Novel Accelerometry Features and Hidden Markov Models Application.

DOI: 10.5220/0005215800760085

In Proceedings of the International Conference on Bio-inspired Systems and Signal Processing (BIOSIGNALS-2015), pages 76-85

ISBN: 978-989-758-069-7

 2015 SCITEPRESS (Science and Technology Publications, Lda.)

tion accuracy (about 99%). The work developed in

(In

es Prata Machado, 2014) also contributed for the

physical activity (PA) recognition in accelerometry

with the K-means technique and also inferred that

one waist-worn accelerometer can identify the phys-

ical activities in an adequate manner. Furthermore,

(Adrian Ball and Velonaki, 2011) states that not only

k-means clustering has a good performance but also

the spectral clustering and the afﬁnity propagation ap-

proaches show high accuracy results.

In the K-means method, the k value (number

of clusters) is deﬁned and k points are chosen

randomly as cluster centers (Ittay Eyal and Rom,

2011),(Ghahramani, 2004), (Liao, 2005). Besides

the K-means method, other clustering methods were

applied in the present work such as Afﬁnity Prop-

agation (Walter, 2007), DBSCAN and Ward (Liao,

2005). The Afﬁnity Propagation clusters dataset

sending messages between pairs of samples until they

converge. These messages represent the suitability

for one data point to be the exemplar (similar) of the

other, which is updated in response to the values from

other pairs (Walter, 2007). In the DBSCAN method,

clusters are areas of high density, separated by regions

of low density, while the Ward method is based on

the minimal variance criterion where two clusters will

be agglomerated into one when a deﬁned value is not

achieved. Otherwise, both clusters will be apart (Liao,

2005).

Clustering techniques applied to biosignals mor-

phology knowledge allows the detection and clas-

siﬁcation of different physical positions and every-

day movements. The clustering stage is crucial for

pathology detection and abnormal behavior evalua-

tion (Nunes, 2011) due to changes that can be de-

tected in the morphology of the accelerometry signal.

Therefore, it is mandatory to acquire enough knowl-

edge and data in order to be able to distinguish be-

tween normal movement patterns and those of certain

pathologies.

1.2 Hidden Markov Models

The Hidden Markov Models (HMM) are statistical

models where the observation is a probabilistic func-

tion of the state. In this case, the observation task is

made by inference and the training set will determine

the transition probabilities between the existing states

(Ping Guo and Wang, 2012),(Fosler-Lussier, 1998).

In (Trabelsi et al., 2013) the HMM were used to

identify the sequence corresponding to 12 physical

activities and the ﬁnal results lead to 91.4 % as a mean

correct classiﬁcation rate averaged over all observa-

tions. They also concluded that the HMM application

leads to a better classiﬁcation rate (84 %) and with

k-means algorithm (60 %). This fact highlights the

potential beneﬁt of automatic identiﬁcation of human

activity with the HMM approach.

1.3 Clustering Performance Evaluation

After the clustering and HMM application, it is pos-

sible to assess if the separation of the data is similar

to the available ground truth set. In an unsupervised

learning context, it is important to create a data anno-

tation as a ground truth (A. M. Khan and Kim, 2010).

In the present work, two clustering performance

evaluation methods were used:

1. Hungarian Accuracy - With two solution sets, the

predicted labels and the ground truth set, it is pos-

sible to measure the distance between both sets.

The labeling of the predicted clusters must cor-

respond to the ground truth available. However,

if two partitions of the dataset are equivalent but

its labelings are represented with different labels,

there will be an ambiguity. To overcome this am-

biguity the labelled indices in one predicted so-

lution are permuted in order to increase the agree-

ment between the two solution sets under compar-

ison. This ambiguity can be minimized through

the Hungarian method with a matrix construction

based on the predicted labels and the ground truth

similarity. This performance evaluation method

measures the fraction of disagreement between

both labels sets through the diagonal of the result-

ing matrix (Kuhn, 2009).

2. Adjust Rand Index - No conjecture is performed

on the cluster arrangement and this technique

measures the similarity between the predicted la-

bels and the ground truth set, ignoring permuta-

tions. The ARI accuracy ranges from 0.0 (0%) to

1.0 (100%), for a perfect score (Clu, 2014).

It is proposed in the present work a framework

implementation for activity recognition through new

features and techniques application, presented in ﬁg-

ure 1.

New features applied to accelerometry, instead of

audio signal alone, might contribute for the discovery

of important movement characteristics never detected

before, such as the CUIDADO features and wavelets.

A new feature inspired in the Mel scale is created and

implemented, called Log Scale Power Bandwidth.

The HMM are also applied to the clustering output

to improve the ﬁnal results of the developed frame-

work.

Section 2 describes the materials and methods

adopted in this work to extract the ACC data for mo-

tion analysis. Section 3 describes the implementation

HumanActivityRecognitionBasedonNovelAccelerometryFeaturesandHiddenMarkovModelsApplication

Figure 1: Overall structure of the framework developed for HAR systems. After the ACC data acquisition, the signal process-

ing and the feature extraction stages are carried out and its results are used in clustering methods and in the HMM application.

Finally, the clustering performance evaluation is applied. The hatched blocks show novel approaches for activity recognition.

of some algorithms that form the developed frame-

work, including the feature selection method and the

Log Scale Power Bandwidth implementation. Section

4 shows the results and respective discussion. Section

5 presents the main conclusions and the take home

message obtained with this work.

2 METHODOLOGY AND

MATERIALS

Two databases were analyzed in the present work:

the online available PAMAP database (PAM, 2014)

and the Foundation Champalimaud Human Activity

(FCHA) database, described in the following subsec-

tions.

2.1 FCHA Database

Seven tasks were carried out by 9 volunteers with an

age range from 23 to 44 years old: standing, sitting,

lying down, walking, running, and ascending and de-

scending stairs. All activities were performed with a

predeﬁned order and time, excluding the ascending

and descending stairs tasks. The walking and run-

ning activities were carried out in a exercise treadmill

with predeﬁned velocities (4 km/h and 10 km/h re-

spectively).

A triaxial accelerometry sensor was located on the

waist with an acquisition frequency sampling of 800

Hz and a resolution of 16 bits. The ACC data acquired

in this protocol formed the FCHA database. Data ac-

quisition was carried out in the Champalimaud Centre

for the Unknown with the OpenSignals software (Ri-

cardo Gomes and Gamboa, 2012).

2.2 PAMAP Database

The PAMAP database is available at (PAM, 2014)

and was also analyzed alongside the FCHA database

in order to verify that the framework created in this

work may suggest encouraging performances even

from acceleration data with different resolutions. The

PAMAP signals were acquired with a sampling fre-

quency of 100 Hz and a resolution of 13 bits. The

PAMAP signals show several physical activities and

nine were selected: standing, sitting, lying down,

walking, running, ascending and descending stairs,

jumping and cycling. The data was acquired from

8 volunteers within an age range 25-31 years and

the 3D-accelerometer sensor used was placed in the

chest. All movement tasks were performed at a vari-

able rhythm, according with each subject in order to

acquire data in the most realistic conditions as possi-

ble.

2.3 Annotation Stage

The annotation document concerns all the labels and

times of each activity performed by a given volunteer.

The initial and ﬁnal time of each activity was anno-

tated in a JSON ﬁle created for each acquired sig-

nal. In addition to the annotation task adopted, the

present work added an extra stage where motion se-

ries of all volunteers were videotaped in order to avoid

erroneous times or labels and for ground truth valida-

tion (Figure 2).

Figure 2: Frames of the subject08’s videotape, performing

four tasks from the protocol: running, lying down, climbing

stairs and cycling.

3 PROPOSED FRAMEWORK

Several algorithms were implemented in this work,

including new previously unused features and the fea-

ture selection method. Algorithms such as the seg-

mentation process and the feature design stage, used

in the present work, were developed previously by our

group (In

es Prata Machado, 2014).

BIOSIGNALS2015-InternationalConferenceonBio-inspiredSystemsandSignalProcessing

3.1 Feature Implementation

There are several features already suggested in other

studies and applied to accelerometry, such as the

Mean and Standard Deviation (Adrian Ball and

Velonaki, 2011),(Ittay Eyal and Rom, 2011), (God-

frey and

OLaighin, 2008), listed in Table 1 with

It is possible to group features according to dif-

ferent parameters, such as time, statistical and fre-

quency domains. However, in the frequency spec-

trum analysis, the FFT does not provide information

about the time at which these frequency components

occur, which leads to the need for a tool that allows

us to analyze the signal on both domains. A wavelet

is a speciﬁc technique for the time-frequency domain

and allows the visualization of the frequency content

over time and consequently a better transient event

description of an accelerometry signal (Godfrey and

OLaighin, 2008), (Jani Mantyjarvi and Seppanen.,

2001). The approximation coefﬁcients from wavelets

decomposition reﬂect the main characteristics of the

signal and these values are used as feature coefﬁcients

in this work (Demetrio Labate and Wilson, 2013),

(Galka and Zi

olko, 2008).

There are other features, called the ”CUIDADO

features”, applied for the ﬁrst time for audio signals

by G. Peeters in (Peeters, 2004) and can be applied

and useful for accelerometry studies. Some from the

CUIDADO features, shown in the Table 1 with

were used in this work. For more information about

these, see (Peeters, 2004).

Table 1 shows all features from the four domains

analyzed in this work.

3.1.1 Log Scale Power Bandwidth

In the present work, the lower frequencies were stud-

ied in more detail than higher frequencies through

logarithmic scales in order to analyze meticulously

the frequency domain. This study was inspired by the

audio spectrum and the Mel scale which ultimately

lead to the feature Log Scale Power Bandwidth cre-

ation.

The Log Scale Power Bandwidth coefﬁcients are

computed and its input is the motion data. This algo-

rithm concerned ﬁve stages:

1. The ﬁrst stage was the pre-emphasizing of the sig-

nal in the time domain. This stage ﬁlters a data se-

quence (the input segment signal) using a digital

ﬁlter which emphasizes the energy of the signal

at high frequencies with a pre-emphasis factor of

0.97;

2. The second step refers to the framing which di-

vides the input data into a set of 3 (M) frames,

Table 1: List of all features used in the present work and

respective domains and number of output coefﬁcients for

each acceleration component: x, y, z, and total accelera-

tion.

Refers to all traditional features already applied in

accelerometry (In

es Prata Machado, 2014);

Refers to the

CUIDADO features used in audio recognition;

Refers to

the new feature type created and implemented in this work.

Feature Type Number of Output

Coefﬁcients (for each

acceleration component)

Statistical

Skewness

Kurtosis

Histogram

Mean

Standard Deviation

Interquartile Range

Time

Root Mean Square

Median Absolute

Deviation

Zero Crossing Rate

Pairwise Correlation

3 (in total)

Autocorrelation

Temporal Centroid

Variance

Frequency

Maximum Frequency

Median Frequency

Power Spectrum

Fundamental

Frequency

Power Bandwidth

Log Scale Power

Bandwidth

Total Energy

Spectral Centroid

Spectral Spread

Spectral Skewness

Spectral Kurtosis

Spectral Slope

Spectral Decrease

Spectral Roll-off

Time-Frequency

Wavelets

each of these with 256 (N) samples;

3. Next, the conversion of the signal segment into the

frequency domain is carried out through the Fast

Fourier Transform application. However, when-

ever a ﬁnite Fourier Transform is applied and if

the start and end of the ﬁnite data do not match,

there will be a discontinuity in the signal. In this

case, there will show up nonsense and undesirable

high-frequencies in the Fourier transform. There-

fore, a windowing stage was computed to the data

sample with a Hamming window to make sure

HumanActivityRecognitionBasedonNovelAccelerometryFeaturesandHiddenMarkovModelsApplication

that the ends match up;

4. A set of triangular overlapping windows in the

range 133-3128 Hz was created. This set of tri-

angular ﬁlters was spaced linearly at lower fre-

quency, below 199 Hz, and logarithmic spaced

above 199 Hz;

5. The triangular ﬁlter bank was applied to the result-

ing data from the step 3 which gives the powers at

each frequency. Finally, the algorithm computes

the log (in base 10) of the powers at each fre-

quency and returned the Log Scale Power Band-

width coefﬁcients as the amplitudes of the result-

ing spectrum.

3.2 Feature Selection

Feature normalization to zero mean and unit vari-

ance is adopted before creating any feature selection.

Next, the most suitable features for activity recogni-

tion were identiﬁed once the feature computation is a

time consuming and computationally heavy task.

The protocol for feature selection is based on the

Forward Feature Selection protocol and aims to select

10 features at most for each clustering method used.

This study may be described by the following steps:

1. Elimination of the redundant information: Corre-

lation between all features and removal of the re-

dundant features. The second feature is removed

when two features show correlation values greater

than or equal to 0.98. The resulting set from this

correlation and elimination stage is called A;

2. Selection of the best ﬁtting features: 20 features

with the highest ARI value are chosen among the

set A, named set B. From the new set formed by

20 features types, B, the feature type with the

highest ARI performance is collected to the set

C, which leaves the original set B with only 19

features. Next, the algorithm combines the set C

with the existing feature types from the set B. The

combination with the highest ARI value and the

corresponding set are identiﬁed. The new feature

belonging to B and to the identiﬁed set is collected

by C. In each iteration, a new feature is deleted

from B and is collected to C. This iterative proce-

dure repeats until C shows the best combination

of 10 features;

3. Saving the ﬁnal results: The algorithm ﬁnishes

the procedure and saves the name and ARI per-

formance of the 10 features set.

3.3 Hidden Markov Models Application

HMM were applied to the clustering results to achieve

higher ARI accuracies in activity recognition. Its

application was formed by the training and testing

stages.

The implemented algorithm uses the ground truth

(true labels) by collecting frequencies of the transi-

tions between all different activities/states and also

deﬁnes the initial state of a given sequence of ac-

tivities through the most frequent initial state. The

frequencies recorded are then converted to the prob-

abilities of the existing symbols and state sequences.

Finally, the testing stage estimates the most probable

sequence of hidden states based in the trained model

and with the Viterbi algorithm.

In the present work, the HMM topology is a com-

pletely connected structure of an ergodic model and

it uses the labels from the clustering methods as test

set and the annotation data (ground truth) as training

set. Finally, the HMM output is used in the clustering

performance evaluation stage.

4 RESULTS AND DISCUSSION

All studies here presented used deﬁned parameters

according to the highest ARI accuracy for each of

these, such as: window segmentation, ﬁltering stage

and wavelet level decomposition. Segments with time

duration of 5 seconds were used as window segmen-

tation. The 8

level was selected as the best level

decomposition for the wavelets algorithm and the ﬁl-

tering stage was eliminated from the signal processing

stage. The referred parameters were used in all stud-

ies presented in the following sections, 4.1, 4.2 and

4.3.

Clustering methods were also selected according

to the ARI performances in order to improve the clus-

tering accuracy. The K-means, Afﬁnity Propagation,

DBSCAN and Ward showed the highest ARI for ACC

data. In parallel to the unsupervised learning tech-

niques, three classiﬁcation methods were used: K-

Nearest Neighbors, Random Forest and Linear Dis-

criminant Analysis (LDA).

4.1 Best set of Features

Feature selection is formed by two stages. The ﬁrst

part aims to ﬁnd the best number of feature types for

activity recognition and second stage aims to identify

which feature types must be used.

The best 10 features were computed from the For-

ward Feature Selection Algorithm, for each subject

BIOSIGNALS2015-InternationalConferenceonBio-inspiredSystemsandSignalProcessing

and clustering method. Figure 3 shows the ARI per-

formance (%) for each number of features and for

each clustering method in order to select the best

number of features to use in ACC recognition. This

study used as ACC data the FCHA database and all

implemented features from four domains: statistical,

time, frequency and time-frequency, presented in Ta-

ble 1.

Figure 3 suggests higher ARI percentages in 4 to

7 features. For a more detailed analysis, table 2 shows

the ARI performances achieved with different cluster-

ing methods when the framework uses the best 4 to 7

feature types (sets A, B, C and D respectively) and all

features.

From table 2, it can be concluded that the set B

is the best set for K-means and Ward performances

with 89.97% ± 9.97% and 88.56% ± 11.72%, respec-

tively. On the other hand, the set C showed higher per-

formance for the DBSCAN method with 80.43% ±

6.29% while the set D showed best performance for

the Afﬁnity Propagation with 81.19% ± 5.99%. For

this reason, any choice from set B, C and D is ac-

ceptable. For K-means, Afﬁnity Propagation, DB-

SCAN and Ward, the clustering performance values

are 84.54% ± 9.23% ,81.19% ± 5.99%, 79.84% ±

10.75% and 84.73% ± 9.00% for set D, 89.97% ±

9.97%, 76.85% ± 10.30%, 78.12% ± 10.95% and

88.56% ± 11.72% for set B. Overall, sets B and D

showed similar computing times (with difference of

approximately 13 seconds) and the best accuracy val-

ues. In the present study set D is the set of the best 7

features and it was chosen for the formed framework.

After selecting the best number as 7 features, the

best group of features was found through a histogram,

where the occurrences of each feature type were rep-

resented. The 10 most used features in each clustering

method were pooled, some of them belonging to the

same feature type, presented in the ﬁgure 4.

The histogram shown in ﬁgure 4 suggested that

the Forward Feature Selection algorithm used with

a higher frequency the Log Scale Power Bandwidth,

Root Mean Square, Total Energy, Autocorrelation,

Variance, Wavelet Coefﬁcients and the Mean for HAR

systems. Therefore, these feature types are the most

used and promising features for the developed frame-

work.

Furthermore, ﬁgure 4 suggested that the Log Scale

Power Bandwidth occurs more frequently (over 20%)

than all the other types of features (with less than 20%

in all occurrences). The Log Scale Power Bandwidth

algorithm involved complex stages and offered a wide

number of coefﬁcients as output. The resulting data

from those 40 output coefﬁcients are complementary.

One particular coefﬁcient tended to be more sensible

in activity distinction due to the variation in behav-

ior over time while the other coefﬁcient may identify

better other different tasks. Thus, by using this type

of feature, all these coefﬁcients are used together and

there will be more information related to the activity

distinction compared with other type of features with

fewer information and lower number of coefﬁcients.

Moreover, the Log Scale Power Bandwidth fea-

ture considered data from the lower frequencies. A

detailed analysis in this frequency range suggested

that there was important information for activity

recognition in accelerometry.The information located

at low frequencies was preserved due to the elimina-

tion of the ﬁltering step in the signal processing stage.

Therefore, no information was lost and the GA com-

ponent was maintained in the ACC data.

It was possible to observe from ﬁgure 5, and in op-

position to others features, that each Log Scale Power

Bandwidth coefﬁcient showed an overall distinction

for all activities carried out by the volunteers. There-

fore the choice of this type of feature from the For-

ward Feature Selection as the best feature is justiﬁed

for its greater ability for activity recognition.

Some difﬁculties referred in (In

es Prata Machado,

2014) such as the hard discrimination between sit-

ting and standing positions and between walking and

running activities were also identiﬁed in this work.

These difﬁculties were subdued due to the presence

of the GA component in the processed data and the

use of the Log Scale Power Bandwidth and Wavelet

coefﬁcients as features. The Horizon Plot in ﬁgure 5

showed the variation of six Log Scale Power Band-

width coefﬁcients, six Wavelet coefﬁcients and one

coefﬁcient of the Autocorrelation, Mean, Root Mean

Square, Total Energy and the Variance from the x-axis

component over time. It is possible to observe that

feature types such as Log Scale Power Bandwidth and

Wavelets are important for the standing and sitting po-

sitions distinction as well in many other tasks.

4.2 Hidden Markov Models Application

All the existing transitions in the test set (predicted la-

bels from the clustering algorithms) with lower prob-

ability of occurrence may be a consequence of clus-

ter miscalculation. These transition probabilities were

gathered from the ground truth (train data) and all

transitions with low probability of occurrence are

avoided and replaced by a more likely transition.

The inﬂuence of the Hidden Markov Model ap-

plication and its improvement (in %) is presented in

ﬁgure 6 and in table 3. All implemented features

were used in this study and only the FCHA database

was analyzed. The improvement values shown in the

HumanActivityRecognitionBasedonNovelAccelerometryFeaturesandHiddenMarkovModelsApplication

Figure 3: ARI performances (%) as a function of different numbers of features (from 1 to 10 features) and according to

different clustering techniques: K-means, Afﬁnity Propagation, DBSCAN and Ward.

Table 2: ARI performances for all features (ﬁrst column) and for the best 4 to 7 features (set A-second, set B-third, set

C-fourth and set D-ﬁfth columns). The last row refers to the time interval used to compute each set of features.

ARI (%)

Clustering Methods All Features Set A Set B Set C Set D

K-means 87.69 ± 5.56 87.37 ± 12.78 89.97 ± 9.97 84.52 ± 7.56 84.54 ± 9.23

Afﬁnity Propagation 78.41 ± 6, 86 76.85 ± 10.30 76.85 ± 10.30 78.33 ± 6.48 81.19 ± 5.99

DBSCAN 78.36 ± 6.96 74.18 ± 9.67 78.12 ± 10.95 80.43 ± 6.29 79.84 ± 10.75

Ward 86.31 ± 8.68 85.96 ± 13.93 88.56 ± 11.72 84.73 ± 9.00 84.73 ± 9.00

Time Response 347.16 107.46 132.13 142.25 145.05

Figure 4: Representation of the Forward Feature Selection results. The algorithm outputted the set of the best 10 features

for each clustering method. Each column corresponds to all occurrences of each feature type in all resulting sets for each

clustering method.

BIOSIGNALS2015-InternationalConferenceonBio-inspiredSystemsandSignalProcessing

Figure 5: Horizon Plot with some feature coefﬁcients from the X-axis acceleration component which vary over time according

to the activity type performed.

Table 3: Clustering performances through ARI without and with the HMM application and its improvement.

Clustering ARI(%) without ARI(%) with Improvement (%)

Methods HMM application HMM application

K-means 81.37 ± 9.83 87.69 ± 5.56 33.92

Afﬁnity Propagation 78.02 ± 9.90 78.41 ± 6.86 1.76

DBSCAN 73.89 ± 12.98 78.36 ± 6.96 17.10

Ward 84.82 ± 8.17 86.31 ± 8.68 9.81

Figure 6: Clustering performances through ARI with and without the HMM application (%) and its performances improve-

ment (%) in bars shown in table 3.

present section is represented by:

Improvement(%) =

100(x

− x

)

100 − x

(1)

where:

- ARI accuracy value without HMM application

- ARI accuracy value with HMM application

The obtained improvement values were 33.92%,

1.76%, 17.10% and 9.81%, for K-means, Afﬁnity

Propagation, DBSCAN and Ward method, respec-

tively. These results deserve a special attention be-

cause there is a signiﬁcant improvement for certain

clustering methods such as the K-means. Thus, it

is important to take into account the HMM applica-

tion together with the adopted unsupervised learning

HumanActivityRecognitionBasedonNovelAccelerometryFeaturesandHiddenMarkovModelsApplication

method. All improvement values were positive and in

this case the HMM algorithm does not provide heavy

computational demands hence it may be applicable to

all demonstrated situations.

4.3 Evaluation of the Performance of

the Framework

After the framework construction, the FCHA

database and the PAMAP database were applied to

the developed algorithms set. In this study, unsuper-

vised and supervised learning approaches were ap-

plied. The tables 4 and 5 showed all results achieved

with the ARI and the HA application and with classiﬁ-

cation methods: Random forest, LDA and K-Nearest

Neighbors, which K value equals the number of activ-

ities carried out. The accuracy score method was used

in classiﬁcation techniques in which the ground truth

was used as training set and the clustering output as

test set.

The PAMAP signals were acquired with a fre-

quency sampling of 100 Hz while the FCHA database

is formed by ACC signals acquired with 800 Hz.

Thus, this data showed different resolutions which in-

ﬂuence the amount of information available for the

clustering and classiﬁcation methods.

Table 4: Clustering evaluation with the ARI and the HA (in

%) for K-means, Afﬁnity Propagation, DBSCAN and Ward.

Clustering (ARI %)

Databases FCHA PAMAP

K-means 84.54 ± 9.23 61.56 ± 13.93

Afﬁnity Propagation 81.19 ± 5.99 63.00 ± 0.19

DBSCAN 79.84 ± 10.75 74.26 ± 16.06

Ward 84.73 ± 9.00 60.53 ± 13.70

Clustering (HA %)

Databases FCHA PAMAP

K-means 89.05 ± 7.43 74.47 ± 8.35

Afﬁnity Propagation 73.20 ± 7.98 83.89 ± 13.65

DBSCAN 76.62 ± 9.68 70.75 ± 10.09

Ward 87.10 ± 8.87 71.13 ± 10.37

Table 5: Classiﬁcation accuracy (in %) with K-Nearest

Neighbors, Random Forest and LDA methods.

Classiﬁcation (Accuracy %)

Databases FCHA PAMAP

K-Nearest Neighbors 97.78 ± 6.67 99.40 ± 1.19

Random Forest 95.39 ± 12.64 97.89 ± 3.89

LDA 98.57 ± 4.30 98.03 ± 2.37

The results obtained within the ARI accuracy

ranged from 79.84% ± 10.75% to 84.73% ± 9.00%

and from 60.53% ± 13.70% to 74.26% ± 16.06% for

the FCHA and PAMAP databases, respectively. On

the other hand, the Hungarian accuracy results ranged

from 73.20% ± 7.98% to 89.05% ± 7.43% and from

70.75% ± 10.09% to 83.89% ± 13.65% for the same

databases. The main cause of the difference between

the two databases may be related to the large differ-

ence in resolution, since the sampling frequencies for

the FCHA base and the PAMAP database are 800 and

100 Hz, respectively. The FCHA data shows eight

times more information than PAMAP data, which

leads to a higher accuracy values. Unlike clustering,

classiﬁcation uses the ground truth for training and

also showed high results: from 95.39% ± 12.64% to

99.40% ± 1.19% for both databases.

More than 85% of the presented results showed an

accuracy higher than 70% which revealed the frame-

works robustness and versatility for activity recogni-

tion with ACC signals, acquired with different sensors

and different resolutions.

4.4 Conclusions

This work aimed to create and develop a novel ges-

ture recognition system based on the consulted litera-

tures concepts and presented in (In

es Prata Machado,

2014).

In the present work and in addition to those used

in our previous work new features were implemented,

such as the Log Scale Power Bandwidth coefﬁcients.

Other features previously used in audio recognition

were also used in ACC data such as the CUIDADO

features and wavelets coefﬁcients. This work offered

a set of 423 feature types for machine learning tech-

niques which provide more and new information re-

garding the performed movement tasks compared to

the literature (Ghahramani, 2004),(Jani Mantyjarvi

and Seppanen., 2001), (Nishkam Ravi and Littman,

2005).

The Forward Feature Selection aimed to reduce

the undesirable redundancy between features and to

select the set of features that may lead to the best

frameworks performance. The chosen features se-

lected as the most suitable feature types for HAR

systems are the Log Scale Power Bandwidth, Root

Mean Square, Total Energy, Autocorrelation, Vari-

ance, Wavelet Coefﬁcients and Mean coefﬁcients.

The achieved results also suggested that it is im-

portant not to waste any information regarding to

movement. The presence of the information located

in lower frequencies, such as the GA component, and

the Log Scale Power Bandwidth implementation may

lead to better static activity distinction.

The obtained results with the FCHA database and

PAMAP database showed that the developed frame-

work is suitable for activity recognition even for ACC

data with a large difference in resolution.

The major achievements of the current work al-

BIOSIGNALS2015-InternationalConferenceonBio-inspiredSystemsandSignalProcessing

lowed to construct a novel HAR system with HMM

which may lead to better performances in activity

recognition. The created framework with a small

number of feature types also ensure high machine

learning results without heavy computational de-

mands. Therefore, as expected accelerometry is a

suitable technique for monitoring movement patterns

in free-living subjects over long periods of time. The

knowledge acquired over this thesis may be applied

into the clinical setting for the diagnosis and physio-

therapy ﬁelds.

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HumanActivityRecognitionBasedonNovelAccelerometryFeaturesandHiddenMarkovModelsApplication