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Smartphone-Based Human Activities Recognition System Using Deep Learning
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Smartphone-Based Human Activities Recognition System Using Deep Learning

A special issue of Sensors (ISSN 1424-8220). This special issue belongs to the section "Intelligent Sensors".

Deadline for manuscript submissions: 15 July 2025 | Viewed by 6712

Special Issue Editor

Dr. Chifumi Iseki

E-Mail Website
Guest Editor
Department of Internal Medicine III, Yamagata University School of Medicine, Yamagata 990-9585, Japan
Interests: life sciences; neurology

Special Issue Information

Dear Colleagues,

Over the past decade, smartphones have revolutionized the way we interact with technology and each other, thanks to their widespread adoption, affordable prices, and advanced sensor capabilities. These devices are now central to many aspects of our daily lives, making them an invaluable tool for research and development in various fields.

This Special Issue aims to explore the potential of smartphones as powerful human activity recognition systems using deep learning techniques. By leveraging the power of smartphones' built-in sensors and the advancements in deep learning, researchers can develop innovative applications to enhance our understanding of human behavior, improve healthcare services, and contribute to a better quality of life.

We invite authors to submit original research articles, works in progress, or surveys on topics related to smartphone-based human activity recognition systems using deep learning. Relevant topics include, but are not limited to, the following:

  • Deep learning algorithms for interpreting smartphone sensor data;
  • Human activity recognition using smartphone sensors and deep learning methods;
  • Context-aware analysis of sensor data for improved activity recognition;
  • Applications of deep learning in healthcare, including injury prevention, rehabilitation, and medication compliance;
  • The evaluation and benchmarking of deep learning methods for human activity recognition;
  • Privacy and security considerations in smartphone-based activity recognition systems;
  • Real-time processing and energy efficiency in deep learning-based activity recognition;
  • The integration of smartphone sensor data with other sources for enhanced activity recognition.

By focusing on the intersection of smartphones, deep learning, and human activity recognition, this Special Issue aims to foster interdisciplinary research and drive innovations that will benefit individuals and society as a whole.

Dr. Chifumi Iseki
Guest Editor

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All submissions that pass pre-check are peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 100 words) can be sent to the Editorial Office for announcement on this website.

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Sensors is an international peer-reviewed open access semimonthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 2600 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

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Published Papers (4 papers)

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Research

17 pages, 2958 KiB  
Article
A Comparative Study of Plantar Pressure and Inertial Sensors for Cross-Country Ski Classification Using Deep Learning
by Aurora Polo-Rodríguez, Pablo Escobedo, Fernando Martínez-Martí, Noel Marcen-Cinca, Miguel A. Carvajal, Javier Medina-Quero and María Sofía Martínez-García
Sensors 2025, 25(5), 1500; https://doi.org/10.3390/s25051500 - 28 Feb 2025
Viewed by 560
Abstract
This work presents a comparative study of low cost and low invasiveness sensors (plantar pressure and inertial measurement units) for classifying cross-country skiing techniques. A dataset was created for symmetrical comparative analysis, with data collected from skiers using instrumented insoles that measured plantar [...] Read more.
This work presents a comparative study of low cost and low invasiveness sensors (plantar pressure and inertial measurement units) for classifying cross-country skiing techniques. A dataset was created for symmetrical comparative analysis, with data collected from skiers using instrumented insoles that measured plantar pressure, foot angles, and acceleration. A deep learning model based on CNN and LSTM was trained on various sensor combinations, ranging from two specific pressure sensors to a full multisensory array per foot incorporating 4 pressure sensors and an inertial measurement unit with accelerometer, magnetometer, and gyroscope. Results demonstrate an encouraging performance with plantar pressure sensors and classification accuracy closer to inertial sensing. The proposed approach achieves a global average accuracy of 94% to 99% with a minimal sensor setup, highlighting its potential for low-cost and precise technique classification in cross-country skiing and future applications in sports performance analysis. Full article
(This article belongs to the Special Issue Smartphone-Based Human Activities Recognition System Using Deep Learning)
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14 pages, 7221 KiB  
Article
Development of a Gait Analysis Application for Assessing Upper and Lower Limb Movements to Detect Pathological Gait
by Atsuhito Taishaku, Shigeki Yamada, Chifumi Iseki, Yukihiko Aoyagi, Shigeo Ueda, Toshiyuki Kondo, Yoshiyuki Kobayashi, Kento Sahashi, Yoko Shimizu, Tomoyasu Yamanaka, Motoki Tanikawa, Yasuyuki Ohta and Mitsuhito Mase
Sensors 2024, 24(19), 6329; https://doi.org/10.3390/s24196329 - 30 Sep 2024
Viewed by 1747
Abstract
Pathological gait in patients with Hakim’s disease (HD, synonymous with idiopathic normal-pressure hydrocephalus; iNPH), Parkinson’s disease (PD), and cervical myelopathy (CM) has been subjectively evaluated in this study. We quantified the characteristics of upper and lower limb movements in patients with pathological gait. [...] Read more.
Pathological gait in patients with Hakim’s disease (HD, synonymous with idiopathic normal-pressure hydrocephalus; iNPH), Parkinson’s disease (PD), and cervical myelopathy (CM) has been subjectively evaluated in this study. We quantified the characteristics of upper and lower limb movements in patients with pathological gait. We analyzed 1491 measurements of 1 m diameter circular walking from 122, 12, and 93 patients with HD, PD, and CM, respectively, and 200 healthy volunteers using the Three-Dimensional Pose Tracker for Gait Test. Upper and lower limb movements of 2D coordinates projected onto body axis sections were derived from estimated 3D relative coordinates. The hip and knee joint angle ranges on the sagittal plane were significantly smaller in the following order: healthy > CM > PD > HD, whereas the shoulder and elbow joint angle ranges were significantly smaller, as follows: healthy > CM > HD > PD. The outward shift of the leg on the axial plane was significantly greater, as follows: healthy < CM < PD < HD, whereas the outward shift of the upper limb followed the order of healthy > CM > HD > PD. The strongest correlation between the upper and lower limb movements was identified in the angle ranges of the hip and elbow joints on the sagittal plane. The lower and upper limb movements during circular walking were correlated. Patients with HD and PD exhibited reduced back-and-forth swings of the upper and lower limbs. Full article
(This article belongs to the Special Issue Smartphone-Based Human Activities Recognition System Using Deep Learning)
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20 pages, 5140 KiB  
Article
MOVING: A Multi-Modal Dataset of EEG Signals and Virtual Glove Hand Tracking
by Enrico Mattei, Daniele Lozzi, Alessandro Di Matteo, Alessia Cipriani, Costanzo Manes and Giuseppe Placidi
Sensors 2024, 24(16), 5207; https://doi.org/10.3390/s24165207 - 11 Aug 2024
Viewed by 3035
Abstract
Brain–computer interfaces (BCIs) are pivotal in translating neural activities into control commands for external assistive devices. Non-invasive techniques like electroencephalography (EEG) offer a balance of sensitivity and spatial-temporal resolution for capturing brain signals associated with motor activities. This work introduces MOVING, a Multi-Modal [...] Read more.
Brain–computer interfaces (BCIs) are pivotal in translating neural activities into control commands for external assistive devices. Non-invasive techniques like electroencephalography (EEG) offer a balance of sensitivity and spatial-temporal resolution for capturing brain signals associated with motor activities. This work introduces MOVING, a Multi-Modal dataset of EEG signals and Virtual Glove Hand Tracking. This dataset comprises neural EEG signals and kinematic data associated with three hand movements—open/close, finger tapping, and wrist rotation—along with a rest period. The dataset, obtained from 11 subjects using a 32-channel dry wireless EEG system, also includes synchronized kinematic data captured by a Virtual Glove (VG) system equipped with two orthogonal Leap Motion Controllers. The use of these two devices allows for fast assembly (∼1 min), although introducing more noise than the gold standard devices for data acquisition. The study investigates which frequency bands in EEG signals are the most informative for motor task classification and the impact of baseline reduction on gesture recognition. Deep learning techniques, particularly EEGnetV4, are applied to analyze and classify movements based on the EEG data. This dataset aims to facilitate advances in BCI research and in the development of assistive devices for people with impaired hand mobility. This study contributes to the repository of EEG datasets, which is continuously increasing with data from other subjects, which is hoped to serve as benchmarks for new BCI approaches and applications. Full article
(This article belongs to the Special Issue Smartphone-Based Human Activities Recognition System Using Deep Learning)
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14 pages, 826 KiB  
Article
GPS Suitability for Physical Frailty Assessment
by Pablo Boronat, Miguel Pérez-Francisco, Arturo Gascó-Compte, Miguel Pardo-Navarro and Oscar Belmonte-Fernández
Sensors 2024, 24(14), 4588; https://doi.org/10.3390/s24144588 - 15 Jul 2024
Viewed by 818
Abstract
The ageing of the population needs the automation of patient monitoring. The objective of this is twofold: to improve care and reduce costs. Frailty, as a state of increased vulnerability resulting from several diseases, can be seen as a pandemic for older people. [...] Read more.
The ageing of the population needs the automation of patient monitoring. The objective of this is twofold: to improve care and reduce costs. Frailty, as a state of increased vulnerability resulting from several diseases, can be seen as a pandemic for older people. One of the most common detection tests is gait speed. This article compares the gait speed measured outdoors using smartphones with that measured using manual tests conducted in medical centres. In the experiments, the walking speed was measured over a straight path of 80 m. Additionally, the speed was measured over 2.4 m in the middle of the path, given that this is the minimum distance used in medical frailty tests. To eliminate external factors, the participants were healthy individuals, the weather was good, and the path was flat and free of obstacles. The results obtained are promising. The measurements taken with common smartphones over a straight path of 80 m are within the same order of error as those observed in the manual tests conducted by practitioners. Full article
(This article belongs to the Special Issue Smartphone-Based Human Activities Recognition System Using Deep Learning)
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