JMIR MEDICAL INFORMATICS

De Ramón Fernández et al

Original Paper

Recognition of Daily Activities in Adults With Wearable Inertial
Sensors: Deep Learning Methods Study
Alberto De Ramón Fernández1, PhD; Daniel Ruiz Fernández1, PhD; Miguel García Jaén2, PhD; Juan M. Cortell-Tormo2,
PhD
1

Department of Computer Technology, University of Alicante, San Vicente del Raspeig, Spain

2

Department of General Didactics and Specific Didactics, University of Alicante, San Vicente del Raspeig, Spain

Corresponding Author:
Daniel Ruiz Fernández, PhD
Department of Computer Technology
University of Alicante
Carretera San Vicente del Raspeig s/n
San Vicente del Raspeig, 03690
Spain
Phone: 34 965 90 9656 ext 3331
Email: druiz@dtic.ua.es

Abstract
Background: Activities of daily living (ADL) are essential for independence and personal well-being, reflecting an individual’sfunctional status. Impairment in executing these tasks can limit autonomy and negatively affect quality of life. The assessmentof physical function during ADL is crucial for the prevention and rehabilitation of movement limitations. Still, its traditional
evaluation based on subjective observation has limitations in precision and objectivity.
Objective: The primary objective of this study is to use innovative technology, specifically wearable inertial sensors combinedwith artificial intelligence techniques, to objectively and accurately evaluate human performance in ADL. It is proposed toovercome the limitations of traditional methods by implementing systems that allow dynamic and noninvasive monitoring of
movements during daily activities. The approach seeks to provide an effective tool for the early detection of dysfunctions andthe personalization of treatment and rehabilitation plans, thus promoting an improvement in the quality of life of individuals.
Methods: To monitor movements, wearable inertial sensors were developed, which include accelerometers and triaxial gyroscopes.
The developed sensors were used to create a proprietary database with 6 movements related to the shoulder and 3 related to theback. We registered 53,165 activity records in the database (consisting of accelerometer and gyroscope measurements), whichwere reduced to 52,600 after processing to remove null or abnormal values. Finally, 4 deep learning (DL) models were createdby combining various processing layers to explore different approaches in ADL recognition.
Results: The results revealed high performance of the 4 proposed models, with levels of accuracy, precision, recall, and F1-score
ranging between 95% and 97% for all classes and an average loss of 0.10. These results indicate the great capacity of the models
to accurately identify a variety of activities, with a good balance between precision and recall. Both the convolutional andbidirectional approaches achieved slightly superior results, although the bidirectional model reached convergence in a smaller
number of epochs.
Conclusions: The DL models implemented have demonstrated solid performance, indicating an effective ability to identify andclassify various daily activities related to the shoulder and lumbar region. These results were achieved with minimal
sensorization—being noninvasive and practically imperceptible to the user—which does not affect their daily routine and promotes
acceptance and adherence to continuous monitoring, thus improving the reliability of the data collected. This research has thepotential to have a significant impact on the clinical evaluation and rehabilitation of patients with movement limitations, byproviding an objective and advanced tool to detect key movement patterns and joint dysfunctions.
(JMIR Med Inform 2024;12:e57097) doi: 10.2196/57097

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KEYWORDS
activities of daily living; ADL; ADLs; deep learning; deep learning models; wearable inertial sensors; clinical evaluation; patient’s
rehabilitation; rehabilitation; movement; accelerometers; accelerometer; accelerometry; wearable; wearables; sensor; sensors;
gyroscopes; gyroscope; monitor; monitoring

Introduction
Activities of daily living (ADL) are the most basic tasks of theperson, as they enable them to function with a minimum ofautonomy. ADL are crucial for maintaining quality of life andpersonal well-being, serving as indicators of functional status[1-3]. ADL are an indicator of a person’s functional status andinclude basic physical tasks such as moving, eating, dressing,maintaining personal hygiene, and grooming, as well as more
complex and instrumental activities such as working, shopping,cleaning, exercising, and participating in recreational activities[2-4]. Impaired physical function can limit the execution ofthese tasks, affecting personal goals and independent living.
This condition can affect the individual’s ability to achieve
personal goals and maintain an independent quality of life
[2,5,6]. Therefore, it is necessary to assess this deterioration
during the execution of ADL in different preventive, clinical,
or rehabilitation contexts [6-8].
The functional assessment of ADL is complex, so it is advisable
to approach it based on the evaluation of fundamental movement
patterns on which these ADL are developed [9-11]. The shoulder
and lumbar region are key joint complexes in this regard.
Specifically, the shoulder joint is essential in many basic ADL,
providing the mobility and stability necessary to perform actions
in all planes of movement. It is essential to position the handin space in a way that allows one to reach objects, eat, buttona shirt, unbutton a bra, or comb one’s hair [9,12-14]. The
movement patterns most used in its assessment arescapula-humeral elevation in the sagittal and frontal plane androtations at different elevation angles [9,13,14]. Similarly, thelumbar region is a joint complex that has a close relationshipwith basic movement patterns such as flexion and extension of
the trunk in the sagittal plane but also in extremely important
actions such as sitting and standing up [10,15-18]. Various ADL
derive from this fundamental movement pattern, the moststudied being the gestures of sitting and getting up from a chair,bending or crouching, and lifting an object or weight [15-17,19].
The precise evaluation, control, and monitoring of ADL
performance are fundamental tasks, although not simple, in thedevelopment of effective intervention tools in these clinical andrehabilitation contexts. Traditionally, the assessment of ADL
has been based on direct observation and subjective evaluationby therapists, which entails biases, errors, and lack of precisionin the results [6,20-22]. In contrast, recent advancements intechnology, including wearable health monitoring devices, smart
clothing sensors, and mobility assistance devices, enable theobjective assessment and quantification of personal performance
during ADL [23-27]. This technology includes wearable devices,motion sensors, and 2D or 3D motion capture systems, whichallow complex movements and functionality of key joints, suchas the shoulder or lumbar region, to be accurately recorded andanalyzed during the performance of ADL [4,9,15]. However,limitations such as its high acquisition and implementation cost,https://medinform.jmir.org/2024/1/e57097

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its specialized technical knowledge, its lack of transparency andcomplexity, or its lack of validation and reliability hinder its
applicability in the specific clinical or rehabilitation context[4,9,24,25].
A promising solution to overcome the aforementioned
limitations is the use of wearable inertial sensors [28-34]. These
have been gaining substantial scientific interest due to theirpotential to provide real-time information on kinematic aspectsof human movement through continuous, dynamic, andminimally invasive monitoring. In the clinical and rehabilitation
field, this technology has emerged as a simple and low-cost
alternative to obtain precise information on accelerations,angular velocities, and trajectories in the different planes of
movement during the execution of different basic ADL. This
technology offers several advantages. It allows for a moreaccurate and objective assessment of the functionality of key
joint complexes, identifying specific areas of weakness orlimitation in movement during ADL and providing quantitative
data on the person’s progress over time [28,35,36]. On the other
hand, it favors the motivation of patients, by being able tovisualize their evolution, thus improving treatment adherence[28,31,37].
However, inertial sensors have some limitations. Despite being
light and small, these devices may not be entirely transparent
for users, especially due to the high number of sensors that, inmany cases, must be used to obtain data that accurately interpret
human movement [30,38,39].
Compared with the traditional approach of most studies that
only use wearable inertial sensors to monitor kinematic aspectsof human movement, the use of artificial intelligence (AI)techniques has been gaining popularity, by helping to improve
the process of assessing and supervising different body
movements using inertial sensors, in addition to reducing thenumber of sensors necessary for this [40-42].
In Yen at al [40], a wearable device consisting of a
microcontroller and an inertial sensor placed on the participant’s
waist is presented. The signals collected by the accelerometerand gyroscope were used to train a 1D convolutional neuralnetwork–based feature learning model, enabling theidentification of 6 ADL. The results demonstrated high accuracy
in both external and study data, validating the effectiveness ofthe proposed method.
The study by Huynh-The et al [41] introduces an innovative
method for recognizing ADL- and sports-related activities using
wearable sensors. This method involves converting inertial data
into color images, facilitating the learning of highly
discriminative features using convolutional neural networks.
Experimental results showed recognition accuracy of over 95%,
outperforming other deep learning (DL)–based approaches forhuman activity recognition (HAR).

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JMIR MEDICAL INFORMATICSIn Ronald et al [43], a novel DL model inspired by the
Inception-ResNet architecture is presented for HAR tasks. The
proposed model, trained on data collected from smartphones
and inertial sensors capturing accelerometer, gyroscope,magnetometer, GPS, temperature, and heart rate signals,
achieved remarkable performance across different data sets,
demonstrating its flexibility and adaptability to varying signal
types and quantities.
Meanwhile, Poulose et al [44] address the challenges of HAR
in health care systems by proposing an approach based on ahuman image threshing machine using smartphone camera
images. The human image threshing system uses mask
region–based convolutional neural networks for human detection
and a DL model for activity classification, achieving a precisionof 98.53% and surpassing conventional sensor-based HAR
approaches.
This study is based on the combination of accelerometer andgyroscope signals with AI techniques for the assessment of theshoulder and lumbar spine. AI algorithms can process the data
captured by inertial sensors and perform sophisticated analyses
to detect patterns, identify alterations in movement, and provide
relevant clinical information. This facilitates a more complete
and accurate evaluation of the joint movement of the shoulder
or lower back, allowing a better understanding of dysfunctions
and personalization of treatment and rehabilitation plans. The
key contributions made by this study are summarized as follows:1.

Accelerometer and gyroscope signals with AI integrationfor enhanced ADL assessment: This combination shows
great potential for the assessment of shoulder and lumbar
region motion in basic ADL performance, providing an
objective and advanced perspective in clinical evaluation
and rehabilitation. However, validly and reliably
demonstrating its use as a control and evaluation tool for
ADL performance, in gestures such as eating, combing hair,
dressing, sitting, or standing, still appears as an unresolved
research challenge. Therefore, in this study, we aim toaddress the automatic detection and monitoring, using AI

De Ramón Fernández et al

2.

3.

4.

5.

techniques, of the patient’s basic ADL related to theshoulder and back.
Enhanced activity recognition precision: Our study relies
on direct capture of inertial sensor signals, potentiallyoffering a more precise and less image quality–dependent
solution.
Efficient sensors use: For signal capture, only 2 sensors areused. Furthermore, it is intended to achieve this objective
through minimal, noninvasive, and practically transparent
sensorization for the user, improving adherence to themonitoring process and facilitating the integration oftechnology into the individual’s daily life at a low cost.
Direct inertial data approach: Our study focuses on the
direct use of accelerometer and gyroscope data without
requiring additional conversion for model training.
Broad scope and versatility: It covers a wide range ofactivities, showcasing its versatility and adaptability.

We believe that this novel approach will make a significant
contribution to this field of research, as it can be used in theprevention, clinical, or rehabilitation contexts of the shoulder
and lumbar region.
The remainder of the paper is organized as follows: the Methods
section addresses how the database was generated, theprocessing layers used, and the architecture of the 4 developed
DL models, as well as the parameters selected for their training
and optimization. In the Results section, the evaluation outcomes
obtained by the 4 DL models are presented, analyzed, andcompared. Finally, the Discussion section presents a discussionof the principal findings and conclusions regarding our study.

Methods
Overview
This research work focuses on the detection and automatic
monitoring of ADL using AI models (DL models) and wearable
inertial sensors to prevent or diagnose injuries, as well as
supervise rehabilitation processes. Figure 1 presents an overview
of the methodology proposed. In the following subsections,each step is explained in depth.

Figure 1. System overview. HDAR: Human Daily Activities Recognition ; IMU: inertial measurement unit.

Ethical Considerations
This study has been conducted in strict accordance with the
ethical principles outlined in the Declaration of Helsinki.
Approval for this research was obtained from the Ethics
Committee of the University of Alicante (protocol code
UA-2023-11-16).
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Prior to commencement, participants provided written informed
consent. Respect for participants, including their autonomy,
confidentiality, and well-being, has been ensured.
All collected data have undergone a rigorous anonymization
process, safeguarding the privacy of the individuals involvedin the research. Protective measures were implemented in
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accordance with institutional guidelines to ensure the securityof participant information throughout the study.
Participants involved in human subjects research were notprovided with any form of compensation. This decision wasmade to uphold transparency and fairness in the research process
and to minimize potential biases associated with compensation.

De Ramón Fernández et al

Data Collection
A total of 9 ADLs were included in the study, 6 of them related
to the shoulder (eating [E], combing hair [CH], fastening thebra [FB], opening the door [OD], reaching for an object [RO],and buttoning up [BU]) and 3 related to the back (sitting [S],standing up [SU], and half squat [HS]). Figure 2 graphicallyshows these movements.

Figure 2. Graphic description of activities of daily living movements. In the top row (from left to right): eating, combing hair, and fastening the bra.
In the middle row (from left to right): opening the door, reaching for an object, and buttoning up. Bottom row (left to right): sitting, standing up, andhalf squat.

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To monitor movements, we used 2 self-developed inertial
measurement unit ERGOtex model sensors [45,46]. This inertial
measurement unit ERGOtex sensor comprises 3 triaxial
accelerometers (±2 g, controlled noise at 100 µg/√Hz), triaxial
gyroscopes (±1000 deg/s, sensitivity error within ±1%, and low
noise level, at ±4 mdeg/s/√Hz), and magnetometers,
encapsulated in a device (weight=8 g, dimensions=23×21×10mm). The ICM-20602 MEMS MotionTracking (TDK Corp)
device was selected for its high-performance specifications,

De Ramón Fernández et al
critical for the reliability of the device. The incorporation of a
1K-byte FIFO buffer reduces serial bus congestion, enhancing
measurement consistency and optimizing device power use. It
operates at a sampling rate of 20 Hz, has an autonomy of 8hours, and can be attached to the skin using double-sided tapeor secured elsewhere using an elastic strap. These enhancements
guarantee reliable response times and sensitivity levels, crucial
for maintaining data accuracy (Figure 3).

Figure 3. ERGOtex inertial measurement unit sensors were developed for movement identification.

The inertial sensors were attached to the skin over the sacrum(S1) and the distal part of the upper extremity (close to thewrist). Primarily designed for monitoring spine posture andarm, this device records acceleration data across all 3 axes.
Internal integration of the acceleration signal occurs within thedevice, transmitting data instantly via Bluetooth (frequency=2.4
GHz) to a smartphone or tablet equipped with the preinstalled
app. This application enables immediate data visualization andfacilitates export to a spreadsheet in comma-separated text
format (CSV).
The database generated initially had 53,165 records of allactivities. The records were grouped into batches of time series(of different lengths) that represented the different movements.
Each record was made up of 12 attributes or numerical variables,
corresponding to the value obtained by the accelerometer andgyroscope of each sensor during the execution of the movement
according to its 3 axes (Acx, Acy, Acz, Gyx, Gyy, Gyz). Afterthe processing stage, where null, missing, and abnormal values
were eliminated, the database was reduced to 52,600 records(RO: n=6423, FB: n=6956, E: n=6216, OD: n=6472, SU:
n=3678, CH: n=6010, BU: n=5915, HS: n=6630, and S:
n=4300).

DL Models

regularization, and dependency capture operations werecombined. The basics of each of them are presented below.

1D Convolution Layer for Feature Extraction
A 1D convolutional layer is specifically designed to processdata that follows a 1D structure, such as time series or text
sequences. In the case of a 1D time series, the 1D convolution
operation follows a similar process as a standard convolutional
layer but is performed along 1 dimension instead of 2 [47]. The
convolution operation is the key component of this type of layer.
During the 1D convolution operation, a filter (kernel) of definedsize slides along the time series, multiplying the filter values
by the corresponding values in the time series and summingthem to produce a single value at the output. This process isrepeated for each filter position throughout the time series, thus
generating a feature map that highlights relevant patterns in thedata sequences. The 1D convolutional layer is essential for the
automatic identification of patterns in time series, allowing
efficient extraction of important features during the training
process. By reducing the number of parameters and avoiding
overfitting, 1D convolution helps capture the temporal structureof data and improve model performance in time series prediction
or classification tasks [48]. Given an input 1D time series X anda set of filters F, the convolution operation is performed asfollows (equation 1):

Processing Layers
To create the DL models, different processing layers that
perform the transformation, regularization, feature extraction,
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where Yi is the output value at the feature map position i, Xi+jis the time series value at position i+j, * denotes the convolution
operation, and bi is the bias associated with the output Fj, andm is the filter size.

Long Short-Term Memory Layer for Modeling Temporal
Dependencies
Long short-term memory (LSTM) layers are a type of recurrent
layer designed to overcome the limitations of traditional
recurrent neural networks in capturing long-term dependencies
in temporal sequences [49]. Its design is based on the idea ofusing internal memory structures controlled by gates to manage
information over time and make decisions about what
information to retain and discard. In an LSTM, 3 main gates
are introduced: the forget gate, which decides what information
should be discarded from the previous memory; the input gate,
which decides what new information should be stored inmemory; and the output gate, which determines what memory
information should be used to generate the output of the layer.
These gates are controlled by activation functions and adjustableweights during training.
An overview of the fundamental equations of an LSTM cell ispresented below, which describe how an LSTM cell manages
information and gates to process and retain relevant information
over time in a temporal sequence, given one input at a time stept, denoted as xt, and the outputs of the previous time step [ht–1](LSTM cell output) and Ct–1 (LSTM cell state).
Forget gate (ft): decides what information should be discarded
or forgotten from the cell state (equation 2)

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Dropout Regularization Layer
The Dropout layer is a regularization strategy that prevents
overfitting by introducing variability into the network during
training [50]. This technique randomly turns off a percentage
of units in each iteration, temporarily removing them and forcing
the network to learn more robust representations. Based on theassembly concept, it simulates the presence or absence of units,
improving effectiveness and reducing dependence on specificunits. In addition to its impact on generalization, the Dropout
layer acts as an effective regularization mechanism, improving
modeling efficiency and performance by preventing
overoptimization and facilitating generalization to unseen data
[51,52].

Flatten and Fully Connected (Dense) Transformation
LayersThe Flatten layer aims to transform 2D or 3D data into a 1D
format, allowing for a more manageable representation andfacilitating the transition from convolutional layers to dense
layers [53]. Given a 3D input matrix where m, n, and p are thespatial dimensions, the Flatten layer converts this matrix into a
1D vector X’ of size m * n * p.
The fully connected (FC) or Dense layer connects all neurons
in 1 layer to all neurons in the next layer [48]. It performs linear
transformations on the data followed by nonlinear activation
functions, allowing complex representations to be learned. If X
is the input of the Dense layer, W is the weight matrix, and b is
the bias vector, the output Y is calculated as (equation 8):

where σ is the activation function.
Input gate (it): decide what new information to store in the cellstate (equations 3 and 4)

The forgotten information and new information are then
combined to update the state of the cell (equation 5).

Output gate (ot): finally, the final activation at the current
position (ht) is calculated with the output gate (ot), which
regulates the amount of information to be output (equation 6)

1D MaxPooling Layer for Feature Reduction
The 1D MaxPooling layer is a technique used in neural networks
to reduce the spatial dimensionality of data by retaining only
the maximum values in specific regions [54,55]. In the context
of 1D time series, 1D MaxPooling is used to summarize themost relevant information and reduce the computational costby decreasing the number of parameters in the network. Given
a 1D input data set X with elements and a pooling window ofsize p, the output Y is calculated by taking the maximum value
in each window. Mathematically, this can be expressed as(equation 9):

where i is the index of the pooling window. This process isrepeated until the entire length of the entry is covered.

Proposed Architecture
Overview
Where σ is the sigmoid function; tanh is the hyperbolic tangent
function; Wi,WC and Wo are weight matrices that are learned
during training; and bf, bi, bc, and bo are biases. [ht–1,xt] denotes
the concatenation of ht–1 and xt before applying the linear
operation.
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The processing layers described above were combined to create
4 DL models of different complexity. Each model was designed
to explore and exploit different approaches in data processing
for the ADL recognition task. The architectures and distinctive
features of each of these models are detailed below.

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Convolutional Approach
The first proposed architecture uses a convolutional approach.
It is composed of 3 main layers: a 1D convolutional layer, apooling layer, and an FC layer (Figure 4). The convolutional
layer, with 64 filters and a kernel size of 5, performs local
feature extraction. Next, the pooling layer with pool size 2 isapplied to reduce the dimensionality and preserve the most
relevant features. Subsequently, a Flatten layer is used to convert
the output into a 1D vector before connecting it to an FC layer

De Ramón Fernández et alwith 128 neurons and a rectified lineal unit (ReLU) activation
function. ReLU is a nonlinear activation function commonlyused in neural networks to introduce nonlinearities and aid inmodel convergence [56]. Finally, a Dropout layer with a rateof 40% is incorporated to prevent overfitting. The output layer
uses the Softmax function and is designed for multiclassclassification. The output layer uses the Softmax function, whichis commonly used in multiclass classification tasks to computethe probabilities of each class outcome and facilitate
decision-making based on the highest probability class [57].

Figure 4. Model architecture based on a convolutional approach.

Deep LSTM Approach
The second architecture is based on a deep LSTM networking
approach. It includes 2 LSTM layers, both with 64 units,
followed by a Flatten layer. Then, 2 FC layers, with 64 and 32
neurons, respectively, and ReLU activation function areincorporated. The output layer uses the Softmax function for

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multiclass classification (Figure 5). This architecture deepens
into the LSTM network with multiple layers, allowing more
complex temporal patterns to be learned. The complexity
increases compared with the convolutional model due to thedeepening of the LSTM layers and the increase in FC
connections. This approach seeks to capture more elaborate
temporal dependencies in time series data.

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Figure 5. Model architecture based on a deep LSTM approach. LSTM: long short-term memory.

Hybrid Approach: 1D Convolutional + LSTM
The third architecture adopts a hybrid approach combining
convolutional layers and LSTM networks (Figure 6). It starts
with a 1D convolutional layer with 32 filters and kernel size 3,
followed by an LSTM layer with 64 units. Subsequently, apooling layer and a Flatten layer are applied. A Dropout layer(30%) is introduced to prevent overfitting before connecting tohttps://medinform.jmir.org/2024/1/e57097

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layer uses Softmax for multiclass classification. This architectureseeks to take advantage of the ability of convolutional layers to
extract local features and the ability of LSTMs to model
long-term temporal dependencies, offering a combination ofboth capabilities. Its complexity lies in the integration of 2
different approaches to improve the representation andunderstanding of time series data.

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Figure 6. Model architecture based on a hybrid approach (convolutional + LSTM). LSTM: long short-term memory.

Bidirectional LSTM Approach
The fourth architecture adopts a bidirectional approach using
LSTM layers (Figure 7). It starts with a bidirectional LSTM
layer with 64 units to capture temporal patterns in both
directions. Then, a Flatten layer is applied before connecting

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with 2 FC layers of 64 and 32 neurons, respectively, with ReLU
activation function. The output layer uses Softmax for multiclassclassification. This architecture represents a more sophisticatedand complex model by taking advantage of the ability ofbidirectional LSTMs to capture both forward and backward
temporal dependencies.

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Figure 7. Model architecture based on a bidirectional LSTM approach. LSTM: long short-term memory.

Selection of Parameters for Training and Optimizationof Models
For a better understanding of the data and selection of thehyperparameters of the AI model, the accelerometry andgyroscope values of each movement were analyzed separately(Figure 8). Based on this, the temporal sequences were divided
into windows of 100 records with a 10-record overlap between
adjacent windows.

Figure 8. Time series of eating activity: (A) accelerometer and (B) gyroscope.

Each model was trained over 150 epochs, representing acomplete iteration through the training data. The model weights
were updated every 1024 records (batch size), and training was
stopped if the validation accuracy did not improve for 15
consecutive epochs (early stopping) to prevent overfitting.
As the optimization algorithm during training, Adam was used.
Its primary goal is to adjust the network’s weights and biases
so that the model’s loss function is minimized. Adam enhances
the standard gradient descent technique by adjusting the learning
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rate for each parameter individually, potentially leading to fasterconvergence and better model performance.
Additionally, an L2 regularization term with a strength of 0.0015
was also applied to mitigate overfitting. This term controls theexcessive growth of weights during training by adding a penalty
term to the model’s loss function. The regularization strength
determines how much large weights are penalized. By penalizing
large weights, L2 regularization helps smooth out the model’s
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De Ramón Fernández et aldecisions and prevents it from fitting too closely to the training
data.
Categorical cross-entropy was used as the loss function. Thisfunction measures the discrepancy between the probability
distributions predicted by the model and the actual distributions.
The primary evaluation metric was accuracy, indicating the
proportion of the model’s predictions in the test set that werecorrect.

Results
Evaluation Metrics
The experiment was performed on a personal computer with
Microsoft Windows 10, an Intel(R) Core(TM) i5-4210U CPU@ 1.70GHz-2.40 GHz, 6 GB RAM, and no GPU. All software
was implemented using the Python programming language andthe TensorFlow library in the Spyder development environment.
After preparing the data, the DL models were trained using 70%of the data, and the remaining 30% was used to evaluate theirperformance. For this, popular evaluation metrics were used inclassification problems, including precision, recall, F1-score,
and accuracy.
Accuracy (equation 10) refers to the proportion of correct
predictions, true positives (TPs) and true negatives (TNs) in
relation to the total predictions made by the model, which
include false positives (FPs) and false negatives (FNs).

Precision (equation 11) represents the proportion of positive
predictions that were correct. It is calculated as the number ofTPs divided by the sum of TPs and FPs.

Recall (equation 12) refers to the proportion of TP cases thatwere correctly identified by the model, calculated as the numberof TPs divided by the sum of TPs and FNs.

F1-score (equation 13) is a measure that combines precisionand recall. It is calculated as the harmonic mean between
precision and recall and provides a more balanced assessmentof model performance, particularly useful when there is an
imbalance in the class distribution in the data.

Evaluation Outcomes
The obtained results show the high performance of the 4
proposed models, with accuracy, precision, recall, and F1-score
ranging between 95% and 97% for all cases (Table 1), while
the loss function indicates an error rate of approximately 0.10
for the models. The high accuracy, precision, and recall suggest
an ability to accurately identify multiple classes of activities,
while the high F1-score indicates a good balance between
precision and recall. These results suggest that the models haveeffectively learned the relationships in the training, enablingthem to identify patterns and generalize effectively to data they
have not encountered during training, demonstrating strong andreliable predictive capabilities.

Table 1. Evaluation metrics of the designed deep learning models.

aModels

Accuracy (%)

Precision (%)

Recall (%)

F1-score (%)

CNNa

97.11

97.19

97.14

97.14

Deep LSTMd

95.52

95.64

95.58

95.54

CNN+LSTM

96.19

96.19

96.14

96.14

Bidirectional LSTM

97.56

97.51

97.52

97.51

CNN: convolutional neural network.

b

LSTM: long short-term memory.

When comparing different modeling approaches, it is evident
that both the convolutional and bidirectional methods yield
similar results across all evaluated metrics. This suggests that,
despite the bidirectional approach’s inherent complexity inprocessing sequences in both directions, it does not offer asignificant improvement over the simpler convolutional method.
The convolutional model may have struck an optimal balance
between learnability and generalization, enabling it to match oreven surpass more complex models in terms of accuracy.
However, it is worth noting that the bidirectional model achieved
convergence in a smaller number of epochs (n=30; Figure 9),https://medinform.jmir.org/2024/1/e57097

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which is particularly valuable when rapid training and model
responsiveness are required.
It is also noteworthy that more complex models, such as deep
LSTM and the hybrid approach, exhibit slightly inferior resultscompared with the convolutional approach. This observation
may stem from several factors. First, the generalization abilityof these models may be compromised due to the inherent
complexity of their architectures and sensitivity to weight
initialization. Additionally, the nature of the data and thesuitability of different modeling approaches to capture therelevant characteristics of the time series should be considered.
The activities represented in the data may benefit more from a
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JMIR MEDICAL INFORMATICS
simpler, more straightforward approach, such as convolutional,
rather than more complex methods that may be prone tocapturing irrelevant features or noise in the data.
At the activity or class level, the confusion matrix provides adetailed breakdown of the model predictions for each classcompared with the real class. Referring to the confusion matrixof the model with the best performance (Figure 10), it isobserved that the majority of the predictions align with the main
diagonal of the matrix, indicating that, for the most part, theclasses are classified correctly. However, the activity of eating

De Ramón Fernández et al
exhibits the most erroneous predictions, primarily being
confused with the activities of opening a door and combing
one’s hair. This confusion may arise due to overlapping
movements and shared characteristics, such as acceleration androtation patterns, making it challenging for the model todistinguish between them. Moreover, variations in the sequenceof movements and the context in which these activities are
performed may lead to different interpretations by the model.
Variability in the execution of activities and differences inmovements between individuals can also contribute to confusion
among these classes.

Figure 9. Training sessions progress over iterations.

Figure 10. Confusion matrix of the winning model based on the bidirectional approach: (left) standard and (right) normalized. BU: buttoning up; CH:
combing hair; E: eating; FB: fastening the bra; HS: half squat; OD: opening the door; RO: reaching for an object; S: sitting SU: standing up.

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Discussion
Background
ADL are fundamental tasks that enable individuals to function
with a minimum of autonomy and maintain their quality of life.
Precise evaluation of ADL, especially in clinical andrehabilitation contexts, is crucial for understanding individuals’
functional status and designing effective interventions.
Traditionally, the assessment of ADL has relied on direct
observation and subjective evaluation by therapists, which canlead to biases and errors. Innovative technology, including
wearable inertial sensors and AI, offers new opportunities forobjective and quantitative evaluation of ADL performance.

Principal Findings
This study presents an innovative initiative by combining
wearable inertial sensors with AI techniques to evaluate human
movement in ADL. The implemented AI models havedemonstrated solid performance, exhibiting high accuracy,
precision, recall, and F1-score (ranging between 95% and 97%),
indicating an effective ability to identify and classify a variety
of daily activities related to the shoulder and lumbar region.
Furthermore, these results have been achieved through minimal
sensorization, which is noninvasive and practically imperceptible
to the user, thus minimizing interference with their daily life.
This feature is crucial as it promotes user acceptance andadherence to continuous monitoring, contributing to thereliability of the collected data.

Comparison to Prior Work
This study presents significant improvements in theidentification and monitoring of activities of ADL comparedwith other existing methods. Unlike most previous approaches
that primarily focus on activities involving the lumbar region(sitting, lying down, standing up, etc), our proposal allows forthe precise identification of complex movements involving boththe lumbar region and the shoulder. This is achieved using only
2 low-cost inertial sensors, contrasting with other solutions that
require a higher degree of sensorization or bulkier devices. This
minimally invasive monitoring enables individuals to performdaily activities naturally, promoting a more authentic
representation of movement.
The information provided by the sensors is used by DL
algorithms for movement identification, without requiring
additional processing. This enables immediate analysis of
movement patterns during the performance of everyday
activities, avoiding the delay associated with data processing
needed in image-based motion capture systems, which tend to
be more expensive and complex to set up and maintain.
Furthermore, the use of inertial sensors offers versatility andadaptability, making them suitable for monitoring a wide rangeof ADL in different environments and contexts. They provide
valuable information on movement patterns and functional

De Ramón Fernández et alabilities that may not be effectively captured or may be moredifficult to capture by traditional 2D and 3D motion capturesystems, which are more limited by factors such as image
quality, potential obstructions in the line of sight between thecamera and the person, or the need to use a greater number ofcameras or sensors to capture all movement details.

Limitations and Strengths of This Study
This study demonstrates notable strengths in its methodology
and approach. It uses the integration of inertial sensors and AI
to improve the assessment of shoulder and lumbar motion during
basic ADL performance, providing an objective and advanced
perspective for clinical evaluation and rehabilitation. Althoughchallenges persist in validating its use across various ADL
gestures, such as eating or dressing, our focus on automatic
detection and monitoring using AI techniques addresses thisgap. Furthermore, by directly capturing inertial sensor signals
and using only 2 sensors, our approach ensures enhanced activity
recognition precision and efficiency. This strategy facilitates
seamless integration into individuals’ daily lives at a low cost,promoting improved adherence to monitoring. Additionally,
our study’s direct use of accelerometer and gyroscope data
without conversion for model training emphasizes its versatility
and broad scope, highlighting its adaptability across a wide
range of activities.
However, it is essential to acknowledge potential limitations toencourage further research and refinement. One limitation liesin the scope of activities monitored, which primarily focuseson specific muscle groups. Future research should aim to expand
the scope of using AI and wearable inertial sensors beyond theassessment of shoulder and lumbar motion, broadening therange of monitored ADL. Given this limitation, it would beinteresting to conduct in the future more extensive studies thatencompass a broader range of ADL and other more distal bodysegments. For instance, investigations could explore theapplication of these technologies in assessing motion patterns
related to limb motion (ie, elbow and wrist, or knee and ankle
movements), offering valuable insights into biomechanicalsegmentary dynamics and enhancing our understanding ofmusculoskeletal movement patterns through AI approaches.
Despite this limitation, the study sets a solid foundation for
future endeavors in this field, showcasing its potential foradvancement and application in clinical and rehabilitative
settings.

Conclusions
This research has the potential to significantly impact the clinicalevaluation and rehabilitation of patients with movement
limitations, offering an objective and advanced tool to detect
key movement patterns and joint dysfunctions. Such information
can assist professionals in tailoring treatment plans to be more
precise and personalized, addressing specific areas of weakness,and designing interventions to improve the patient’s
functionality and quality of life.

Acknowledgments
This research was funded by the Valencian Innovation Agency of Spain (grant INNVA1/2020/81).

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De Ramón Fernández et al

Data Availability
The datasets generated during this study are not publicly available due to ethical restrictions but are available from the corresponding
author on reasonable request.

Authors' Contributions
DRF and JCT designed the research. JCT and MGJ collected and supervised the data. ADR analyzed the data and developed andevaluated the deep learning models. All authors drafted the manuscript. DRF and JCT critically reviewed the manuscript. DRF
had primary responsibility for the final content. All authors read and approved the final manuscript.

Conflicts of Interest
None declared.

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Abbreviations
ADL: activities of daily living
AI: artificial intelligence
BU: buttoning up
CH: combing hair
DL: deep learning
E: eating
FB: fastening the bra
FC: fully connected
FN: false negative
FP: false positive
HAR: human activity recognition
HS: half squat
OD: opening the door
LSTM: long short-term memory
RO: reaching for an object
ReLU: rectified lineal unit
S: sitting
SU: standing up
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TN: true negative
TP: true positive

Edited by C Lovis; submitted 05.02.24; peer-reviewed by A Poulose, M Maximiano, J Colado Sanchez; comments to author 01.03.24;
revised version received 27.03.24; accepted 30.06.24; published 09.08.24
Please cite as:
De Ramón Fernández A, Ruiz Fernández D, García Jaén M, Cortell-Tormo JM
Recognition of Daily Activities in Adults With Wearable Inertial Sensors: Deep Learning Methods StudyJMIR Med Inform 2024;12:e57097
URL: https://medinform.jmir.org/2024/1/e57097
doi: 10.2196/57097
PMID: 39121473©Alberto De Ramón Fernández, Daniel Ruiz Fernández, Miguel García Jaén, Juan M. Cortell-Tormo. Originally published in
JMIR Medical Informatics (https://medinform.jmir.org), 09.08.2024. This is an open-access article distributed under the termsof the Creative Commons Attribution License (https://creativecommons.org/licenses/by/4.0/), which permits unrestricted use,
distribution, and reproduction in any medium, provided the original work, first published in JMIR Medical Informatics, is properly
cited. The complete bibliographic information, a link to the original publication on https://medinform.jmir.org/, as well as thiscopyright and license information must be included.

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