Human Activity Recognition using Deep Learning Models on

Smartphones and Smartwatches Sensor Data

Bolu Oluwalade

, Sunil Neela

, Judy Wawira

, Tobiloba Adejumo

and Saptarshi Purkayastha

Department of BioHealth Informatics, Indiana University-Purdue University Indianapolis, U.S.A.

Department of Radiology, Imaging Sciences, Emory University, U.S.A.

Federal University of Agriculture, Abeokuta, Nigeria

Keywords: Human Activities Recognition (HAR), WISDM Dataset, Convolutional LSTM (ConvLSTM).

Abstract: In recent years, human activity recognition has garnered considerable attention both in industrial and academic

research because of the wide deployment of sensors, such as accelerometers and gyroscopes, in products such

as smartphones and smartwatches. Activity recognition is currently applied in various fields where valuable

information about an individual’s functional ability and lifestyle is needed. In this study, we used the popular

WISDM dataset for activity recognition. Using multivariate analysis of covariance (MANCOVA), we

established a statistically significant difference (p < 0.05) between the data generated from the sensors

embedded in smartphones and smartwatches. By doing this, we show that smartphones and smartwatches

don’t capture data in the same way due to the location where they are worn. We deployed several neural

network architectures to classify 15 different hand and non-hand oriented activities. These models include

Long short-term memory (LSTM), Bi-directional Long short-term memory (BiLSTM), Convolutional Neural

Network (CNN), and Convolutional LSTM (ConvLSTM). The developed models performed best with watch

accelerometer data. Also, we saw that the classification precision obtained with the convolutional input

classifiers (CNN and ConvLSTM) was higher than the end-to-end LSTM classifier in 12 of the 15 activities.

Additionally, the CNN model for the watch accelerometer was better able to classify non-hand oriented

activities when compared to hand-oriented activities.

1 INTRODUCTION

Over the past decade, smartphones have become an

important and indispensable aspect of human lives.

More recently, smartwatches have also been widely

accepted as an alternative to conventional watches,

which is referred to as the quantified self movement

(Swan, 2013). Even in low- and middle-income

countries, smartphones have been embedded in the

social fabric (Purkayastha et al., 2013), even though

smartwatches haven’t. Thus, working on both

smartphone and smartwatch sensor is still quite

relevant. Smartphones and smartwatches contain

sensors such as accelerometer, gyroscope, GPS, and

much more. These sensors help capture activities of

daily living such as walking, running, sitting, etc.

and is one of the main motivations for many to own

a smartwatch. Previous studies have shown

accelerometer and gyroscope to be very effective in

recognition of common human activity (Lockhart et

al., 2011). In this study, we classified common human

activities from the WISDM dataset through the use of

deep learning algorithms.

The WISDM (Wireless Sensor Data Mining) Lab

in the Department of Computer and Information

Science of Fordham University collected data from

the accelerometer and gyroscope sensors in the

smartphones and smartwatches of 51 subjects as they

performed 18 diverse activities of daily living (Weiss,

2019). The subjects were asked to perform 18

activities for 3 minutes each while keeping a

smartphone in the subject’s pocket and wearing the

smartwatch in the dominant hand. These activities

include basic ambulation related activities (e.g.,

walking, jogging, climbing stairs), hand-based daily

activities (e.g., brushing teeth, folding clothes), and

various eating activities (eating pasta, eating chips)

(Weiss, 2019). The smartphone and smartwatch

contain both accelerometer and gyroscope sensors,

yielding a total of four sensors. The sensor data was

collected at a rate of 20 Hz (i.e., every 50ms). Either

one of Samsung Galaxy S5 or Google Nexus 5/5X

smartphone running the Android 6.0 was used. The

LG G Watch was the smartwatch of choice. A total of

Oluwalade, B., Neela, S., Wawira, J., Adejumo, T. and Purkayastha, S.

Human Activity Recognition using Deep Learning Models on Smartphones and Smartwatches Sensor Data.

DOI: 10.5220/0010325906450650

In Proceedings of the 14th International Joint Conference on Biomedical Engineering Systems and Technologies (BIOSTEC 2021) - Volume 5: HEALTHINF, pages 645-650

ISBN: 978-989-758-490-9

645

15,630,426 raw measurements were collected.

We answer three main research questions in the

analysis of this data. Firstly, are there significant

differences in how the two devices capture data, even

though they both contain similar accelerometer and

gyroscope sensors? Secondly, what is the best method

to recognize the activities that were performed by the

subjects through this sensor data? Thirdly, by

using an identified activity, how accurately can we

forecast or simulate the activities? We conducted a

MANCOVA analysis, which showed that there is a

statistically significant difference between the data

obtained from the accelerometer and gyroscope in the

smartphone and smartwatch. After separating the

smartphone and smartwatch data, we classified the

activities using neural network architectures,

including Long short-term memory (LSTM), Bi-

directional Long short-term memory (BiLSTM),

Convolutional Neural Network (CNN), and

Convolutional LSTM (ConvLSTM). Finally, we

developed a GRU model to forecast the last 30

seconds of the watch accelerometer’s raw values and

calculated the accuracy metrics of this forecasting

with the actual values.

RELATED WORKS

Due to the increase in the availability of several

sensors like accelerometer and gyroscope in various

consumer products, including wearables, there has

been a rise in the number of research studies on human

activity recognition (HAR) using sensor data. In one

of the earliest HAR studies, Kwapisz et al. used

phone accelerometers to classify 6 human activities,

including walking, jogging, climbing the stairs,

walking down the stairs, sitting, and standing using

machine learning models like logistic regression and

multilayer perceptron (Kwapisz et al., 2011). Their

models recognized most of the activities with an

accuracy of 90%. Esfahni et al. created the PAMS

dataset containing both smartphone’s gyroscope and

accelerometer data (Esfahani and Malazi, 2017).

Using the section of the data collected from holding

the phone with non-dominant hand, they developed

multiple machine learning models to identify the

same six activities as Kwapisz et al., and obtained a

precision of more than 96% for all the models.

Random forest and multilayer perceptron models

outperformed the rest, with a precision of 99.48% and

99.62% respectively. These results were better than

the ones obtained from data collected when the phone

was held in the dominant thigh (Esfahani and Malazi,

2017). Also, Schalk et al. obtained more than 94%

accuracy for same activities as above by developing a

LSTM RNN model (Pienaar and Malekian, 2019).

Agarwal et al. proposed a LSTM-CNN Architecture

for Human Activity Recognition learning model for

HAR. This model was developed by combining a

shallow RNN and LSTM algorithm, and its overall

accuracy on the WISDM dataset achieved 95.78%

accuracy (Agarwal and Alam, 2020). In addition,

previous studies like Walse et al. (Walse et al., 2016)

and Khin (Oo, 2019) have also used the WISDM

accelerometer data to classify a maximum of 6

activities in their work. Although the above models

could generally recognize human activities, they were

evaluated on their ability to recognize just six human

activities and therefore do not provide generalization.

Our study addresses these shortcomings by

developing several deep learning algorithms for 15

human activities recorded in the WISDM data. We

select the best model based on the F1 score, i.e.,

considering both precision and recall. Here, we

obtained an average classification accuracy of more

than 91% in our best performing model. Additionally,

we try to simulate the data 30 seconds into the future

and provide metrics that might be used by other

researchers to build more generalizable models.

METHODOLOGY

3.1

WISDM Dataset Description

The WISDM dataset contains raw time series data

from phone and watch’s accelerometer and gyroscope

(X-, Y-, and Z-axis). The raw accelerometer’s and

gyroscope’s signals consist of a value related to each

of the three axes. The raw data was segmented into

10-second data without overlapping. Three minutes

(180 Seconds) of raw data was divided into eighteen,

10-seconds segment where each segment’s range was

calculated to obtain 18 values. This was further

divided into 10 equal-sized bins to give X 0-9; Y 0-9,

Z0-9. Features were generated based on these 10 bins

obtained the raw accelerometer and gyroscope

readings. Binned distribution, average, standard

deviation, variance, average absolute difference, and

time between the peaks for each axis were calculated.

Apart from these, other features, including Mel-

frequency cepstral coefficients, cosine distance,

correlation, and average resultant acceleration, were

calculated but are not used in this study. After this

preprocessing, we finally had the following entries for

each of the device sensors:

•

phone accelerometer: 23,173

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•

phone gyroscope: 17,380

•

watch accelerometer: 18,310

•

watch gyroscope: 16,632

Also, the activities are divided into two classes: non-

hand and hand-oriented activities.

•

Non-hand-oriented activities: walking, jogging,

stairs, standing, kicking, and sitting

•

Hand-oriented activities: dribbling, playing

catch, typing, writing, clapping, brushing teeth,

folding clothes, drinking and eating

3.2

MANCOVA Analysis

Multivariate Analysis of (Co)Variance (MANCOVA)

explores the relationship between multiple dependent

variables, and one or more categorical and/or

continuous outcome variables. To perform the

MANCOVA analysis on our data, we used the X-, Y-

, Z-axis data of the accelerometer and gyroscope at

each time epoch as the dependent variables. The

categorical variables were the phone and watch. We

defined our null hypothesis that there is no

statistically significant difference between the phone

and watch data. The alternate hypothesis was that

there is a statistically significant difference between

the phone and watch data. The null hypothesis was

rejected as we obtained a statistically significant

difference between the phone and the watch data (p

less than 0.05).

3.3

Classification Models

The MANCOVA analysis showed a significant

difference between the phone and watch data.

Therefore, we created separate classification models

for the phone and watch. We selected 44 features from

the watch and phone data with an activity label

attached to each row in the dataset. The activities

include walking, jogging, walking up the stairs,

sitting, standing, typing, brushing teeth, eating soup,

eating chips, eating pasta, drinking, eating

sandwiches, kicking, playing catch, dribbling a ball,

writing, and clapping. Thus, a total of 18 activities. We

combined all the different eating activities to form a

combined ”eating” activity. This reduced the number

of activities to 15. We used the Keras package in

Python for the experiments. The architecture of the

watch accelerometer classification models is

described below. In each case of training the models,

we stopped the epochs when the training loss became

equal to the validation loss to prevent overfitting:

3.3.1

Long Short-term Memory (LSTM)

Networks

Hochreiter and Schmidhuber originally introduced

LSTMs (Hochreiter and Schmidhuber, 1997), and

were refined and popularized later (Sherstinsky, 2018)

(Zhou et al., 2016). LSTMs are a special kind of

recurrent neural networks (RNNs) capable of learning

long-term dependencies. This quality in the network

architecture helps to remember certain useful parts of

the sequence and helps in learning parameters more

efficiently. The scaled dataset was input into the

LSTM model containing 128 LSTM units, followed

by a dropout layer (0.3 units), dense layer (64 Units),

dropout layer (0.2 units), dense layer (64 units), dense

layer (32 units) and a last dense layer with 15 units

(for the number of classes). We used Softmax as the

activation function in the last layer, ReLU for the

previous layers, and the Adam optimizer. The loss was

calculated in categorical cross-entropy. The model

was trained for 226 epochs with a batch size of 32.

3.3.2

Bi-directional Long Short-term

Memory (BILSTM) Networks

We also implemented a BiLSTM model to observe

the effects of either direction on performance. In the

BiLSTM, we fed the data once from the beginning to

the end and once from the end to the beginning. By

using two hidden states in BiLSTM, we can preserve

information from both the past and the future at any

point in time. The parameters of our BiLSTM model

is similar to the LSTM model.

3.3.3

Convolutional LSTM

Xingjian introduced convolutional LSTM’s (Shi et

al., 2015) in 2015. Convolutional LSTMs

(ConvLSTM) are created by extending the fully

connected LSTM to have a convolutional structure in

both the input-to-state and state-to-state transitions.

ConvLSTM network captures spatiotemporal

correlations better and outperforms Fully Connected

LSTM networks.

The scaled data is reshaped and inputted to a 1-

dimensional convolution layer with 128 filters of

kernel size 4 followed by a dropout layer (0.4), LSTM

layer with 128 units, Dense layer with 100 units,

Dense layer with 64 units, Dropout layer with 0.2

dropout rate, Dense layer with 32 units, and finally a

Dense layer with 15 units for classification. We used

Softmax as the activation function in the last layer,

ReLU for the previous layers, and Adam optimizer.

The loss was calculated in categorical cross-entropy.

Human Activity Recognition using Deep Learning Models on Smartphones and Smartwatches Sensor Data

647

The model was trained for 95 epochs with a batch size

of 32.

3.3.4

Convolutional Neural Network (CNN)

The data is reshaped and inputted to a 1-dimensional

convolution layer with 128 filters of kernel size 10

followed by a dropout layer (0.4), 1-dimensional

convolution layer with 128 units and 10 kernel size,

dropout layer with 0.2 dropout rate, 1-dimensional

max-pooling layer with 0.2 pool size, flatten layer,

dense layer with 64 units and finally a Dense layer

with 15 units for classification. We used Softmax as

the activation function in the last layer, ReLU for the

previous layers, and Adam optimizer. The loss was

calculated in categorical cross-entropy. The model

was trained for 148 epochs with a batch size of 32.

RESULTS

4.1

Classification Results

To classify the activities, we utilized four classifiers,

namely Long short-term memory (LSTM), Bi-

directional Long short-term memory (LSTM),

Convolutional Neural Network (CNN), and

Convolutional LSTM (ConvLSTM). The Precision

and F1 scores were used as evaluation metrics to

analyze the performance of the classifiers.

Table 1: The Macro-F1 values of different classifiers for

watch sensor data.

Models Acceleromete

Gyroscope

Both

CNN 0.849

0.687

0.774

BiLSTM 0.848

0.617

0.721

ConvLSTM 0.843

0.658

0.754

LSTM 0.825

0.627

0.743

Table 2: The Macro-F1 values of different classifiers for

phone sensor data.

Models Accelerometer Gyroscope

Both

CNN 0.796

0.387

0.631

BiLSTM 0.773

0.429

0.611

ConvLSTM 0.814

0.432

0.638

LSTM 0.756

0.395

0.743

Tables 1 and 2 demonstrate the Macro-F1 values

of the different classifiers for the watch and phone

data. From the above tables, the classifiers performed

better with the watch data. Also, the classifier

performed better with accelerometer than gyroscope

data. Thus, the classifiers performed best on the watch

accelerometer data with a Macro-F1 measure of

0.849, 0.848 and 0.843 and 0.825 for the CNN,

BiLSTM, ConvLSTM, and LSTM models,

respectively. These classifiers’ performance was not

greatly different from each other, with all the

classifiers having a Macro-F1 measure of more than

0.80 (80%) with the CNN marginally performing the

best with a Macro-F1 measure of 0.849 (84.9%).

The watch accelerometer data were divided into

training, testing, and validation data, respectively, in

0.8, 0.1, and 0.1 ratios. These data contain 14648,

1831, and 1831 records for training, testing, and

validation, respectively. The classifiers’ precision

values on the watch accelerometer data separated into

hand oriented and non-hand oriented classes are

presented in Tables 3 and 4 below.

Table 3: The precision values of different classifiers for

watch accelerometer data (Non hand-oriented activities).

Activities CNN BiLSTM ConvLSTM

LSTM

Mean

Walking 0.861 0.869

0.925

0.846

0.875

Jogging 0.833 0.859

0.865

0.872

0.857

Stairs 0.909 0.939

0.900

0.862

0.903

Sitting 0.918 0.836

0.844

0.851

0.862

Standing 0.917 0.818

0.669

0.760

0.791

Kicking

0.926 0.857 0.867

0.857

0.877

Mean

0.894 0.863 0.845

0.841

0.861

Table 4: The precision values of different classifiers for

watch accelerometer data (Hand-oriented activities).

Activities CNN BiLSTM ConvLSTM LSTM

Mean

Typing 0.862 0.773 0.838

0.671

0.786

Brushing 0.981 0.955 0.963

0.972

0.968

Drinking 0.867 0.808 0.801

0.768

0.811

Eating 0.757 0.884 0.677

0.779

0.774

Catch 0.850 0.871 0.870

0.906

0.874

Dribbling 0.844 0.802 0.797

0.768

0.802

Writing 0.807 0.816 0.906

0.836

0.841

Clapping 0.785 0.902 0.758

0.769

0.804

Folding 0.821 0.830 0.871 0.794

0.829

Mean 0.842 0.849 0.831 0.807

0.832

Tables 3 and 4 show that the precision of the

convolutional input classifiers (CNN and

ConvLSTM) was higher than the end-to-end LSTM

classifiers. The convolutional classifiers gave the best

classifications in 12 of 15 activities irrespective of the

class of the activities (i.e., non-hand-oriented and

hand-oriented activities). The convolution input

layer has also been shown to outperform conventional

fully connected LSTM in capturing spatiotemporal

correlations in data (Shi et al., 2015). Another fact

that can be inferred from the table 3 above is that the

classifiers perform better in the non-hand-oriented

activities like standing, stairs, etc. than the hand-

oriented activities like eating, clapping, etc. with the

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exception of brushing teeth, which has the highest

precision values of all the classifiers.

4.2 Forecasting Results

In addition to the classification models, we also

predicted the last 30 seconds of the raw data for all the

eating activities. To achieve this, we utilized a

gated recurrent unit (GRU) model. We used the Root

Mean Square Error (RMSE), Mean Square Error,

Mean Absolute Percentage Error (MAPE), and

symmetric Mean Absolute Percentage Error (sMAPE)

as evaluation metrics.

Table 5: The different performance metrics of different

activities for phone data.

Activities

RMSE

MSE

MAPE

sMAPE

H (eating soup)

0.078

0.00603

126.580

40.57

I (eating chips)

0.070

0.00494

11.43

10.59

J (eating pasta)

0.039

0.00152

7.263

7.25

K (drinking

from cup)

0.083

0.00687

13.080

13.296

L (eating

sandwich)

0.050

0.00247

4.45

4.54

Mean

0.064

0.0044

32.56

15.25

Table 5 shows that GRU gave the best forecast for

eating sandwiches when compared to other activities

like drinking from a cup, eating pasta, eating chips,

eating soup, etc. Also, it can be inferred from Table 5

that the MAPE overstated the error found in activity

H because of the presence of outliers when compared

to the values of other activities with their respective

sMAPE.

DISCUSSION

The statistically significant difference (p < 0.05)

between the same kind of sensors in smartphones and

smartwatches using a MANCOVA analysis points to

some interesting observations. This is likely due to the

location of the pocket in comparison to the hand.

However, there is more to it than just the differences

in the X-, Y-, and Z-axis values due to the height of

the sensors from the ground. Our analysis showed that

the difference between the peaks and the throughs was

also larger in the smartwatch. Furthermore,

identifying a constant that differentiated the X-, Y-, or

Z-axis between the two devices was practically

impossible. This means that the difference between

the sensors is not purely due to the height from the

ground. Following this distinction, we created various

deep learning models to classify 15 different activities

based on the smartwatch accelerometer data only. Our

findings suggest that non-hand-oriented activities like

standing, stairs, etc. are better generalized and

better classified than hand-oriented activities like

eating, clapping, etc. with the exception of brushing

teeth as the classifiers performed better and had

higher precision values. This implies that the

smartwatch accelerometer data can better classify

non-hand oriented activities even if the smartwatch is

located on the dominant hand. A recent study by

Agarwal et al. (Agarwal and Alam, 2020), used a

lightweight RNN-LSTM architecture to classify 6

different non-hand oriented activities based on

smartphone accelerometer data. They obtained an

average of 0.9581 (95.81%) precision. Our best

model obtained a precision to 92.6% for kicking

and 98.1% for brushing teeth. We obtained an

average of 89% precision for 6 non-hand oriented

activities recognition and 84% for 9 hand oriented

activities. However, these are not directly

comparable, as we also calculated the Macro-F1

values, which consider both the precision and recall

i.e., when our model accurately classifies the activity

but also fails at classifying the activity. This should

be considered as a better measure for HAR.

Moreover, we also did better at HAR compared to

other papers that used the WISDM data and used

LSTM-CNN, and shallow RNN. Accurate HAR is

clinically relevant, not only because individuals have

started using wearables widely, but also because there

are many clinically-relevant sensors such as the

BioStamp MC10, fall detection devices, and

telemedicine sensors. These sensors might also be

useful to identify mental health issues (Addepally and

Purkayastha, 2017) or motor function disorders (Ellis

et al., 2015) using mHealth apps. All these devices

have accelerometer and gyroscope sensors to

understand patient’s gait, posture, and stability for

accurate measurement of other clinical features. As

home healthcare, senior home care and health care

outside the hospital settings become more common,

the application of HAR is becoming more relevant.

We also created a GRU model to forecast the last

30 seconds of raw data generated by the watch

accelerometer for 4 eating activities based on the

previous 210 seconds data. We obtained an average

RMSE of 0.064, which implies a minimal difference

from the actual values. Thus, we can say that

generating such sensor data for generalizing models

might also be a feasible approach for retraining

models or transfer learning of models to similar

sensors of other devices. This is a future direction that

we are pursuing.

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LIMITATIONS

Although the use of classification models such as

Long short-term memory (LSTM), Bi-directional

Long short-term memory, Convolutional Neural

Network (CNN) and Convolutional LSTM, etc., is a

fairly common approach to predicting the movement

of a person, this study does not provide the needed

generalization with the hand-oriented activities. We

have not, for example, examined differences in the

performance metrics of the eating activity when

forecasting the raw values of the last 30 seconds of

the watch accelerometer.

CONCLUSION

In this study, we classified smartphone and

smartwatch accelerometer and gyroscope data. We

classified the majority of the activities using artificial

neural network algorithms, including Long short-term

memory (LSTM), Bi-directional Long short-term

memory, Convolutional Neural Network (CNN), and

Convolutional LSTM. Our classification analysis on

15 different activities resulted in an average

classification accuracy of more than 91% in our best

performing model. Although previous findings

indicated that 6 human activities were used during the

analysis, our study followed several 15 human

activities, which are better generalized than those in

major studies conducted previously. It is possible that

outcomes would vary if over 20 or 25 human

activities are used. Future researchers should

consider investigating the impact of more human

activities. Nonetheless, our results provide the needed

generalization for non-hand oriented activities

recognition cases only.

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