Comparison of Machine Learning Algorithms for

Human Activity Recognition

Hassan Ashraf

, Olivier Brüls

, Cédric Schwartz

and Mohamed Boutaayamou

Laboratory of Movement Analysis (LAM-Motion Lab), University of Liège, Liège, Belgium

Keywords: Human Activity Recognition, Daily-Life Activity Classification, Machine Learning, Pattern Recognition,

Wearable Sensors, Inertial Sensors, Accelerometer, Gyroscope, IMU Signals.

Abstract: Human activity recognition (HAR) is utilized to automatically identify the daily-life activities of people for

the effective management of age-related health conditions. Classical machine learning (ML) algorithms are

used to design HAR systems, in a subject-specific or population-based configuration depending on the

application. In this study, the performance of 8 classical and ensemble-learning-based ML classifiers has been

studied for both HAR configurations. Inertial measurement unit (IMU) signals from 10 healthy participants,

corresponding to various static, dynamic, and transitional daily-life activities, were acquired. Random forest

(RF), ensemble adaptive boosting (EAB), ensemble subspace (ES), decision tree (DT), k-nearest neighbors

(KNN), linear discriminant analysis (LDA), support vector machine (SVM), and artificial neural network

(ANN) were used to classify these activities. The performance of the classifiers was measured in terms of

mean classification accuracy (MCA). The results showed that, for a subject-specific HAR system, ES

(97.78%) has achieved the highest MCA followed by RF (96.61%) and SVM (96.11%) while outperforming

the DT, KNN, and LDA (P-value < 0.05). For a population-based HAR system, SVM (95.18%) achieved the

highest MCA, however, no significant difference has been observed among the MCA of all the investigated

classifiers (P-value > 0.05). Also, the class-wise comparison reveals that SVM outperformed the other

investigated classifiers in terms of MCAs for each of the distinct activities. Based on the HAR configuration

incorporating diverse static, dynamic, and transitional daily-life activities, the findings may be used to develop

a customized HAR system for the effective management of movement disorders.

1 INTRODUCTION

According to the International Diabetes Foundation

(IDF), the global diabetes prevalence in adults aged

20 to 79 years old is expected to be 536.6 million in

2021, rising to 783.2 million in 2045 (Atlas, 2015).

Similarly, more than 10 million people worldwide are

living with Parkinson’s disease (PD) and the

incidence of PD increases with age (Tysnes &

Storstein, 2017). Such an aging population needs

care. Smart healthcare systems seem to be a possible

answer to the rising aging population dilemma. They

can provide smart health services to meet the needs of

this rising population by monitoring and analysing

any critical health state of the elderly in their daily

https://orcid.org/0000-0003-0519-0619

https://orcid.org/0000-0003-2668-1353

https://orcid.org/0000-0002-7307-7821

https://orcid.org/0000-0001-9564-528X

activities. Smart healthcare systems not only allow

older people to live autonomously, but they may also

offer more sustainable healthcare solutions by

reducing the strain placed on the entire health system

by the aged and dependent persons.

Human activity recognition (HAR) is a prominent

research topic that can give a solution to such a

challenge by playing an important role in healthcare,

particularly in medical diagnosis and fitness

monitoring. Accurate assessment of physical activity

is therefore critical in establishing intervention

methods, as it provides rich contextual information

from which more important information may be

inferred. HAR may also be used for people with a

mental ailment or disease, such as Parkinson's

162

Ashraf, H., Brüls, O., Schwartz, C. and Boutaayamou, M.

Comparison of Machine Learning Algorithms for Human Activity Recognition.

DOI: 10.5220/0011631500003414

In Proceedings of the 16th International Joint Conference on Biomedical Engineering Systems and Technologies (BIOSTEC 2023) - Volume 4: BIOSIGNALS, pages 162-169

ISBN: 978-989-758-631-6; ISSN: 2184-4305

 2023 by SCITEPRESS – Science and Technology Publications, Lda. Under CC license (CC BY-NC-ND 4.0)

disease, to monitor their actions regularly and notice

any abnormalities (Church, 2021).

Machine learning (ML) or pattern recognition

methods are primarily used to process signals for the

development of HAR applications. Irrespective of the

chosen ML method, the data is processed in two

stages, i.e., training the ML model on the pre-

recorded dataset and then testing the trained model on

unseen data. The HAR signal processing with ML

methods involves data acquisition, signal pre-

processing, feature extraction, and classification.

Each of the subsequent steps of the signal processing

pipeline is of crucial importance to capture the

desired information and extract patterns. Along with

different choices for each subsequent signal

processing step, a HAR system can be designed in

two configurations i.e., subject-specific and

population-based, depending on the application. In

subject-specific HAR systems the training and testing

data is utilized from the same subject, whereas, in

population-based HAR systems the model is trained

on data acquired from multiple subjects (population)

and tested on new subjects.

Among other parameters, the selection of the

classifier is of utmost importance since its objective

is to map extracted features into distinct classes.

Various researchers have done tremendous work to

identify the impact of each of the parameters on the

performance of the HAR system. Ambati et al. 2020

conducted a comparative study for a smartphone-

based HAR system to evaluate the performance of

different ML classifiers including naïve Bayes (NB),

logistic regression (LR), decision tree (DT), and

random forest (RF) using 3 different HAR datasets

(Ambati & El-Gayar, 2020). The study reported that

RF performed better than the rest of the utilized ML

algorithms, however, the study did not report the

utilized HAR configuration. Similarly, another study

also conducted a comparative analysis to evaluate the

performance of different ML classifiers for mobile-

based HAR applications to recognize 6 different daily

life activities (Min et al, 2020).

For a subject-specific HAR system, the study

reported that the RF algorithm outperforms the rest of

the ML classifiers (Min et al., 2020). Another study

compared LR, support vector machine (SVM), DT,

and RF for a 6-class population-based HAR system

and reported that SVM outperformed all the other ML

classifiers by achieving a validation accuracy of

96.57% (Muralidharan et al., 2021). Logacjov et al.

(2021) presented a body-worn sensors-based HAR

dataset from 22 participants (Logacjov et al., 2021).

For a population-based HAR system with leave-one-

out validation the performance of k-nearest neighbors

(KNN), SVM, RF, and XGBoost (XGB) was

compared. The results demonstrated that SVM

outperformed the rest of the algorithms with an F1

score of 0.81±0.18. Baldominos et al. (2019)

performed a comparative analysis of ML techniques

for a mobile phone-based HAR system (Baldominos

et al., 2019). The data were recorded from 10 healthy

participants for 13 daily life activities and the

performance of ensemble trees (ET), NB, KNN, LR,

artificial neural network (ANN), and RF was

compared for a population-based HAR system. The

results demonstrate that ET outperformed the rest of

the ML algorithms by achieving an accuracy of

94.87%. Another study was conducted by Attal et al.

(2015) from a dataset of 6 healthy subjects for 12

different HAR activities (Attal et al., 2015). For a

subject-specific HAR system KNN, SVM, and RF

classifiers were used to classify different HAR

activities. The results report that KNN outperforms

the rest of the algorithm with an accuracy of 96.53 ±

0.2. Vijayvargiya et al. (2021) compared the

performance of KNN, LDA, bagging classifier

(BagC), boosting classifier (BosC), DT, RF, and

SVM for population-based HAR system by

concluding that RF yields the best results with an

accuracy of 92.71% (Vijayvargiya et al., 2021).

Table 1: Daily life activities performed by each participant.

ID Activity type Activity Description

Dynamic

Walk normal

(

)

Walkin

and turnin

at normal s

eed on a flat surface.

Walk dual task (WD)

Walking and turning at normal speed while carrying a

tra

and havin

lasses.

3 Walk slow

(

)

Walkin

and turnin

at a slow s

eed on a flat surface.

4 Walk fast (WF) Walking and turning at a fast speed on a flat surface.

5 Stairs descending (SD) Descending the stairs.

6 Stairs ascending (SA) Ascending the stairs.

Transitional

Walk and sit

(

WSit

)

Walkin

towards the chair, sittin

, and standin

8 Walk and la

down

(

WLa

Walkin

towards the bed, la

down, and standin

Static

Sittin

on a chair

(

Sit

)

Sittin

on a chair.

10 Laying on a

ed (Lay) Laying on a bed.

Comparison of Machine Learning Algorithms for Human Activity Recognition

163

Although various researchers have done great

work to identify the best ML algorithm for the

development of HAR applications by conducting

both subject-specific and population-based studies.

However, there is no consensus about which ML

algorithm is best suitable for both HAR

configurations and there is a lack of simultaneous

comparison of both configurations for the data

recorded from the same population. For

generalization, we hypothesize that it is important to

compare different ML algorithms on the same

population (subjects) for both HAR configurations

due to the data-driven nature of the ML algorithms.

Secondly, statistical significance and class-wise

performance should also be taken into consideration

while evaluating the performance of the algorithms.

Finally, the performance of the ensemble learning-

based ML classifiers should also be investigated

along with classical ML classifiers.

Thus, this study aims to identify, among many

available ML algorithms, which is best suited to HAR

applications for both configurations (subject-specific

and population-based). Furthermore, to statistically

validate the results a one-way analysis of variance

(ANOVA) test was also conducted.

2 METHODOLOGY

2.1 Dataset

The experiment was conducted on ten healthy

subjects (one female and nine males; average age

(years) = 26.6±1.7; average height (cm) = 174.0±5.9;

average weight (kg) = 69.6±6.3) without having any

gait or movement disorders. Before recording the

data, the participants were informed about the

experimental protocol and they were instructed to

follow their natural pattern of daily-life activities. The

experimental protocol consisted of ten different static,

dynamic and transitional activities as described in

table 1. All the participants completed the designed

daily-life activities in a lab environment (Laboratory

of Movement Analysis (LAM-Motion Lab),

University of Liège, Liège, Belgium) by following a

structured experimental protocol. For each daily-life

activity, the subjects performed 5 repetitions.

An existing IMU-based hardware system was

used to record the movement signals (Boutaayamou

et al., 2019). Four customized wired-IMU sensors

were placed on the left heel, right heel, left wrist, and

lower back. To minimize the movement of IMU

sensors all sensors were tightly attached to the body.

Each IMU sensor (2 cm × 0.7 cm × 0.5 cm) with a

sampling frequency of 200 Hz was equipped with a

three-axis accelerometer (range: ±16 g) and a three-

axis gyroscope (range: 2000 degrees/second). All the

IMU sensors, through wires, were connected to an

integrated system comprised of a system-integrated

memory, a microcontroller, and a battery. Once the

data is recorded, all the data were transferred to a

computer for further processing and analysis.

MATLAB 2022a has been used to process and

generate the results.

2.2 Pre-Processing

Most of the spectral power of human body

movements is concentrated between 0 to 20 Hz

(Wohlfahrt, 2012). Furthermore, the signals recorded

by accelerometers are a combination of acceleration

due to the movement of the body, acceleration due to

gravity, the noise which is intrinsic to the

measurement system, and motion artifacts. Only the

acceleration signals (body acceleration and gravity

acceleration) are required for the analysis in most of

the applications, whereas other components are

regarded as unwanted noise (Awais, 2018).

Depending on the application and types of noise

embedded in the original signals various digital filters

can be utilized to minimize the effects of unwanted

noise. For HAR applications both acceleration signals

are quite useful thus it is only desired to separate any

spectral content beyond the spectral range of human

body movements. In this study, a third-order

Butterworth lowpass filter with a cut-off frequency of

20 Hz was applied to the acquired signals to remove

the unwanted frequency components. Figure 1 depicts

the raw and filtered signal both in the time and

frequency domains. It can be observed that with the

application of the applied digital filter the frequencies

above 20 Hz have been discarded.

2.3 Segmentation/Windowing

The sensors attached to the body supply a continuous

stream of signals acquired from the human body. To

analyse and process them, these signals are

segmented into segments of finite length. Primarily

disjoint and overlap windowing/segmentation

techniques are utilized to make segments of the

signals under consideration. Before segmentation, it

is important to consider the variable durations of

different human body movements. For example,

transitional activities (e.g., sit-to-stand) are

completed in less time as compared to static (e.g.,

standing) or dynamic activities (e.g., walking).

Intuitively, smaller segments or window sizes capture

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164

Figure 1: The raw and filtered acceleration signal in the time and frequency domain. The upper graph shows the acceleration

signal in the time domain, the lower graph shows the frequency spectrum of the raw signal.

Table 2: ML classifiers and the training parameters.

Classifie

Trainin

arameters

Random forest (RF)

Number of decision splits = 942

Number of learning cycles = 30

Ensemble adaptive boosting (EAB)

Number of decision splits = 20

Number of learning cycles = 30

Learnin

rate = 0.1

Ensemble subspace (ES)

Learner = Discriminant analysis

Number of learnin

cles = 30

Decision tree

(

)

Number of decision s

lits = 100

-nearest neighbors (KNN) Number of nearest neighbors = 1

Linear discriminant analysis (LDA)

Discriminant type = linear

Amount of re

ularization = 0

Support vector machine (SVM)

Box constraint = 1

Kernel function = linea

Artificial neural networks (ANN)

Hidden layers = 18

Trainin

function = scaled con

ate

radient

the underlying patterns of transitional activities while

missing the information necessary to capture the

patterns of dynamic and static activities. Similarly,

longer window sizes provide better results for static

and dynamic activities and perform poorly for

transitional activities since the longer window sizes

overlap the important information of transitional

activities also these windows are very prone to noise.

Furthermore, longer windows require more

computational power as compared to smaller window

sizes. Yamansavaşçılar & Güvensan (2016)

suggested that, for HAR activities, a window size of

more than 6 s is sufficient to capture the underlying

patterns of the movements (Yamansavaşçılar &

Güvensan, 2016). Thus, in this study, an overlap

windowing technique with a window size of 6 s and

an overlap size of 60% has been employed.

2.4 Feature Extraction

After the signals have been segmented, they must be

transformed into feature space. The goal of the feature

space is to minimize the dimensionality of the

original data and extract the descriptive hidden

Comparison of Machine Learning Algorithms for Human Activity Recognition

165

underlying information of movement patterns,

making it easier to map the complicated data into

predetermined categories. The collected features

should have enough data to train the machine learning

algorithms. Various temporal, frequency, and time-

frequency domain features have been reported to

enhance the efficacy of a HAR system (Rosati et al.,

2018). In this study, the investigated features are:

mean, root mean square, autocorrelation features for

all three axis components (height of the main peak;

height and position of the second peak), spectral peak

features (height and position of the first 6 peaks),

spectral power features (total power in 3 adjacent and

pre-defined frequency bands of 1.5, 5, and 10 Hz) and

signal magnitude area.

2.5 Classification

The feature space is utilized as an input to the

classifier after extracting relevant information from

the segments. The classifier creates the final

mappings from the characteristics associated with

each class. To classify daily-life activities for various

HAR applications, multiple ML classifiers such as

NB, DT, RF, SVM, LDA, KNN, and ANN are often

employed. The performance of several ML

algorithms (classical and ensemble-learning-based)

has been evaluated, as the goal of this work is to

determine the most suited ML classifier for subject-

specific and population-based HAR systems. Table 2

describes the explored ML algorithms as well as the

training parameter choices.

In subject-specific HAR configuration, 70% of

the data from each subject was randomly selected to

train the classifier, and the remaining 30% of the data

was utilized to assess the trained classifier's

performance. A leave-one-out validation technique,

on the other hand, has been used for a population-

based HAR system. The data from nine participants

were initially concatenated and fed into the classifier

for training, then the data from the last subject was

utilized to evaluate the developed ML model. The

technique continued until all of the participants, one

by one, were tested. To assess the performance of

each classifier, the classification accuracy (CA) has

been calculated based on the actual and predicted

results. CA is a percentage that is calculated by

dividing the proportion of accurate predictions by all

possible predictions and multiplying the result by

100. To further validate the results, statistical analysis

has been undertaken by using ANOVA with Tuckey’s

honest post-hoc test to reject the null hypothesis by

considering a P-value of 0.05 significant.

3 RESULTS

3.1 Subject-Specific HAR System

Table 3 presents the CAs for all subjects

corresponding to each investigated classifier. The

cells with bold syntax represent the highest achieved

testing accuracy for each subject. For all the subjects

RF, EAB, ES, and SVM obtained more than 90% CA.

The results indicate that ES achieved the highest

accuracies for most of the subjects (nine subjects)

followed by RF (one subject). Although ES has

obtained the highest CAs for most of the subjects the

RF, EAB, and SVM have also achieved comparable

results.

Mean classification accuracy (MCA) was

calculated by averaging the CA for all subjects

corresponding to each investigated classifier. MCA

for all subjects showed that ES has achieved the

highest MCA of 97.78% followed by RF (96.61%)

and SVM (96.11%). Furthermore, statistical

analysis revealed that ES has outperformed the DT,

KNN, and LDA (P-value < 0.05). However, no

significant difference in MCA of ES, RF, EAB,

SVM, and ANN has been observed (P-value > 0.05).

It can be observed that DT, LDA, KNN, and ANN

performed poorly for SD, SA, WSit, and WLay

activities. Furthermore, although ES, RF, SVM, and

EAB have no statistically significant difference in

MCAs, however, still ES is the only classifier

obtaining more than 90% accuracy for all the

individual activities. Despite having no statistically

significant difference in MCA of ES, RF, EAB,

SVM, and ANN (P-value > 0.05), ES has resulted in

higher CAs for all the classes.

3.2 Population-Based HAR System

In the population-based HAR system, the ML

classifiers were trained on data combined from nine

subjects and tested on the data from the remaining

subject. Table 4 presents the CAs for the population-

based HAR system corresponding to each testing

subject. The highest CAs for individual testing

subjects have been attained by SVM (three testing

subjects), ES (three testing subjects), and RF (two

testing subjects).

All the classifiers attained more than 90%

accuracy corresponding to at least one of the

investigated classifiers except one testing subject

(TSID = 8). According to statistical analysis, SVM

and DT obtained maximum and minimum MCAs

of 95.18% and 86.33%, respectively. However, no

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Table 3: CA (%) for all the subjects corresponding to each investigated ML classifier for a subject-specific HAR system. The

first column represents the subject ID (SID). The cells highlighted in bold represent the classifier with the highest CA for

each subject.

SID RF EAB ES DT KNN LDA SVM ANN

1 98,3 96,0 98,7 94,0 94,4 98,0 96,0 91,7

2 97,9 97,5 97,9 93,6 94,0 93,6 97,2 97,9

3 97,9 95,4 98,7 92,4 91,6 94,1 97,0 96,6

4 95,3 95,3 97,2 88,1 91,3 91,7 96,0 94,9

5 96,1 96,1 97,2 89,0 91,5 92,9 95,8 94,3

6 95,7 94,5 97,9 86,0 94,9 88,9 96,2 93,2

7 96,9 96,9 97,3 93,9 92,9 95,9 96,9 96,6

8 94,8 93,1 97,0 86,6 86,6 77,9 92,2 88,7

9 99,3 96,0 98,9 93,8 95,2 98,5 97,1 97,1

10 93,8 93,4 97,1 91,8 94,7 93,0 96,7 95,1

Mean ± St

96,6 ± 1,7 95,4 ± 1,3 97,8 ± 0,7 90,9 ± 3,0 92,7 ± 2,5 92,5 ± 5,6 96,1 ± 1,4 94,6 ± 2,7

significant difference in the MCA of all the evaluated

classifiers for the population-based HAR system was

identified (P-value > 0.05). SVM and DT resulted in

the best and worst class-wise performance results,

respectively. All the investigated classifiers are

performing poorly in at least one of the classes,

except SVM. Regardless of the testing subject data or

signal class, SVM was able to classify all classes with

substantially greater accuracy.

4 DISCUSSION

The study aimed to investigate the performance of

classical and ensemble learning based on different

ML classifiers to design subject-specific and

population-based HAR systems. Both HAR

configurations were designed based on IMU data

recorded from 10 healthy volunteers. The data was

comprised of various static, dynamic and transitional

daily-life activities. For the first time, we explored the

efficacy of ensemble-learning-based ML classifiers

for both HAR configurations and compared the

performance with classical ML classifiers.

The findings for the subject-specific HAR system

demonstrated that ES has obtained the highest MCA

of 97.78% while outperforming the rest of

investigated ML classifiers. Statistical investigation

revealed no significant difference in the performance

of ES, RF, EAB, SVM, and ANN (P-value > 0.05).

Most of the literature, regarding the selection of a

classifier for a subject-specific HAR system, suggests

that RF, KNN, and SVM are the best suitable ML

classifiers (Attal et al., 2015; Logacjov et al., 2021;

Min et al, 2020; Muralidharan et al., 2021). Our

findings are consistent with past research in this area,

suggesting that RF delivers higher performance

results. However, the results showed that ES

outperforms RF in terms of MCA. ES has not only

outperformed the other classifiers in terms of MCA

but also, achieved higher classification rates in terms

of class-wise MCA. The average class-wise CA for

all the investigated classifiers showed that only ES

has achieved more than 90% MCA for all the

individual classes. Intuitively, it is difficult for any

HAR system to differentiate among transitional

activities since the underlying patterns of the

movement signals are so similar. For example, the

WSit class incorporates both walking and sitting,

which is similar to the classes that require both

walking and sitting. Because of this resemblance, it is

difficult to distinguish transitory activities from the

rest of the activities. Except for ES, all of the tested

classifiers resulted in decreased MCA for transitional

activities (SD, SA, WSit, and WLay). Based on these

findings, it can be concluded that in a subject-specific

HAR system, ES delivers the best classification

results in a subject-specific HAR system.

SVM resulted in the highest MCA for a

population-based HAR system with an overall MCA

of 95.18%. Statistical analysis has revealed that there

is no significant difference in MCAs of all the

investigated classifiers (P-value < 0.05). Previous

literature on population-based HAR systems also

indicates that SVM and RF are the best ML classifiers

in terms of MCA (Baldominos et al., 2019;

Logacjovet al., 2021; Muralidharan et al., 2021;

Vijayvargiya et al., 2021). However, overall MCA is

not the only indicator of the performance measure

since it does not provide any insight into class-wise

performance. Although SVM, LDA, ES, and RF have

comparable performance in terms of MCA, however,

the class-wise performance of these classifiers is

vastly different. From figure 3 it is evident that RF,

ES, and LDA are performing very poorly in

transitional and dynamic activities. Conversely, SVM

Comparison of Machine Learning Algorithms for Human Activity Recognition

167

Table 4: CA (%) for all the subjects corresponding to each investigated ML classifier for a population-based HAR system.

The first column represents the testing subject ID (TSID). The cells highlighted in bold represent the classifier with the highest

CA for each subject.

TSID RF EAB ES DT KNN LDA SVM ANN

1 94,4 87,1 94,4 85,1 93,1 94,7 99,0 94,4

2 95,0 95,4 95,7 92,9 86,9 94,3 95,0 93,6

3 95,0 94,1 98,7 91,2 85,3 98,3 98,3 95,8

4 95,2 94,0 96,8 88,1 92,5 95,2 98,0 98,8

5 96,2 94,1 95,5 92,0 86,0 94,1 97,2 96,9

6 93,6 87,1 94,0 84,1 82,8 92,7 93,6 92,7

7 92,2 75,7 81,8 71,3 84,5 79,1 95,3 80,4

8 75,3 77,1 83,5 69,7 86,6 89,2 80,5 80,5

9 98,2 93,8 97,5 93,5 86,2 92,0 95,6 85,5

10 100,0 97,5 99,2 95,5 88,9 99,6 99,2 98,8

Mean ± St

93,5 ± 6,4 89,6 ± 7,3 93,7 ± 5,8 86,3 ± 8,6 87,3 ± 3,1 92,9 ± 5,4 95,2 ± 5,2 91,7 ±6,7

is obtaining consistent MCA for static, dynamic, and

transitional activities. Except for SVM, all the other

investigated classifiers yielded a high

misclassification rate for at least one of the activities.

For example, ANN and LDA resulted in high MCA

for all the activities, however, both classifiers

misclassified WN activity with WS and/or WF. From

these findings, it can be concluded that for a

population-based HAR system, SVM provides better

classification results in terms of overall and class-

wise MCA.

While comparing the two configurations, it was

discovered that the performance of the ML classifiers

varies dramatically. Figure 4 illustrates the MCA for

all classifiers examined in both configurations.

Switching from a subject-specific HAR system to a

population-based HAR system reduces the MCA for

all ML classifiers except LDA. Although it is a well-

known fact that ML algorithms perform better when

trained on big datasets, a drop in accuracy has been

reported for a population-based HAR system. One

possible reason for this phenomenon is the increased

variance of the dataset owing to the inclusion of data

from various subjects. Since every human has

different movement patterns, it is challenging for the

ML algorithm to capture the between-subject

variation for any daily-life activity. Despite the higher

between-subject variation, LDA and SVM performed

consistently in terms of MCA for all subjects for both

configurations. The difference between subject-

specific and population-based HAR systems is quite

small in both circumstances. Further research should

be done to determine how the number of individuals

affects the performance of a population-based HAR

system.

Despite the results providing a basic

understanding of the choice of ML classifier to design

a HAR system with subject-specific and population-

based configuration, the limitations of the study are:

(1) the utilized dataset should be increased further by

including more number of subjects, (2) number of

daily-life activities, more specifically, complex daily-

life activities should also be considered in future, (3)

since the dataset has been recorded in a controlled

environment thus the unstructured and uncontrolled

daily-life activities may induce a higher variability in

the results.

5 CONCLUSIONS

The study presented the comparison of classical and

ensemble learning-based ML classifiers to design

subject-specific and population-based HAR systems

to classify diverse daily-life activities. The movement

data were acquired using custom-designed IMU

sensors from ten healthy participants for different

static, dynamic and transitional activities. The results

demonstrated that, for both subject-specific and

population-based HAR systems, ES and SVM are the

best ML classifiers in terms of overall MCA and

class-wise performance. For the subject-specific

HAR system, the results demonstrated that ES

outperforms RF and all other investigated classifiers

by obtaining higher overall and class-wise MCA. The

ES and SVM-based proposed HAR systems can be

used to recognize intricate daily-life activities for the

development of a smart healthcare system.

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