Research

Jump to: Review

14 pages, 2224 KiB

Open AccessArticle

Identification of a Gait Pattern for Detecting Mild Cognitive Impairment in Parkinson’s Disease

by Michela Russo, Marianna Amboni, Paolo Barone, Maria Teresa Pellecchia, Maria Romano, Carlo Ricciardi and Francesco Amato

Sensors 2023, 23(4), 1985; https://doi.org/10.3390/s23041985 - 10 Feb 2023

Cited by 5 | Viewed by 1956

Abstract

The aim of this study was to determine a gait pattern, i.e., a subset of spatial and temporal parameters, through a supervised machine learning (ML) approach, which could be used to reliably distinguish Parkinson’s Disease (PD) patients with and without mild cognitive impairment [...] Read more.

The aim of this study was to determine a gait pattern, i.e., a subset of spatial and temporal parameters, through a supervised machine learning (ML) approach, which could be used to reliably distinguish Parkinson’s Disease (PD) patients with and without mild cognitive impairment (MCI). Thus, 80 PD patients underwent gait analysis and spatial–temporal parameters were acquired in three different conditions (normal gait, motor dual task and cognitive dual task). Statistical analysis was performed to investigate the data and, then, five ML algorithms and the wrapper method were implemented: Decision Tree (DT), Random Forest (RF), Naïve Bayes (NB), Support Vector Machine (SVM) and K-Nearest Neighbour (KNN). First, the algorithms for classifying PD patients with MCI were trained and validated on an internal dataset (sixty patients) and, then, the performance was tested by using an external dataset (twenty patients). Specificity, sensitivity, precision, accuracy and area under the receiver operating characteristic curve were calculated. SVM and RF showed the best performance and detected MCI with an accuracy of over 80.0%. The key features emerging from this study are stance phase, mean velocity, step length and cycle length; moreover, the major number of features selected by the wrapper belonged to the cognitive dual task, thus, supporting the close relationship between gait dysfunction and MCI in PD. Full article

(This article belongs to the Special Issue Feature Papers in Wearables 2022)

► Show Figures

Figure 1

12 pages, 6187 KiB

Open AccessArticle

ECG Identification Based on the Gramian Angular Field and Tested with Individuals in Resting and Activity States

by Carmen Camara, Pedro Peris-Lopez, Masoumeh Safkhani and Nasour Bagheri

Sensors 2023, 23(2), 937; https://doi.org/10.3390/s23020937 - 13 Jan 2023

Cited by 3 | Viewed by 1817

Abstract

In the last decade, biosignals have attracted the attention of many researchers when designing novel biometrics systems. Many of these works use cardiac signals and their representation as electrocardiograms (ECGs). Nowadays, these solutions are even more realistic since we can acquire reliable ECG [...] Read more.

In the last decade, biosignals have attracted the attention of many researchers when designing novel biometrics systems. Many of these works use cardiac signals and their representation as electrocardiograms (ECGs). Nowadays, these solutions are even more realistic since we can acquire reliable ECG records by using wearable devices. This paper moves in that direction and proposes a novel approach for an ECG identification system. For that, we transform the ECG recordings into Gramian Angular Field (GAF) images, a time series encoding technique well-known in other domains but not very common with biosignals. Specifically, the time series is transformed using polar coordinates, and then, the cosine sum of the angles is computed for each pair of points. We present a proof-of-concept identification system built on a tuned VGG19 convolutional neural network using this approach. We confirm our proposal’s feasibility through experimentation using two well-known public datasets: MIT-BIH Normal Sinus Rhythm Database (subjects at a resting state) and ECG-GUDB (individuals under four specific activities). In both scenarios, the identification system reaches an accuracy of 91%, and the False Acceptance Rate (FAR) is eight times higher than the False Rejection Rate (FRR). Full article

(This article belongs to the Special Issue Feature Papers in Wearables 2022)

► Show Figures

Figure 1

10 pages, 934 KiB

Open AccessArticle

Validation of SuPerSense, a Sensorized Surface for the Evaluation of Posture Perception in Supine Position

by Daniela De Bartolo, Ilaria D’amico, Marco Iosa, Fabio Aloise, Giovanni Morone, Franco Marinozzi, Fabiano Bini, Stefano Paolucci and Ennio Spadini

Sensors 2023, 23(1), 424; https://doi.org/10.3390/s23010424 - 30 Dec 2022

Cited by 2 | Viewed by 1185

Abstract

This study aimed to validate a sensorized version of a perceptive surface that may be used for the early assessment of misperception of body midline representation in subjects with right stroke, even when they are not yet able to stand in an upright [...] Read more.

This study aimed to validate a sensorized version of a perceptive surface that may be used for the early assessment of misperception of body midline representation in subjects with right stroke, even when they are not yet able to stand in an upright posture. This device, called SuPerSense, allows testing of the load distribution of the body weight on the back in a supine position. The device was tested in 15 patients with stroke, 15 age-matched healthy subjects, and 15 young healthy adults, assessing three parameters analogous to those conventionally extracted by a baropodometric platform in a standing posture. Subjects were hence tested on SuPerSense in a supine position and on a baropodometric platform in an upright posture in two different conditions: with open eyes and with closed eyes. Significant correlations were found between the lengths of the center of pressure path with the two devices in the open-eyes condition (R = 0.44, p = 0.002). The parameters extracted by SuPerSense were significantly different among groups only when patients were divided into those with right versus left brain damage. This last result is conceivably related to the role of the right hemisphere of the brain in the analysis of spatial information. Full article

(This article belongs to the Special Issue Feature Papers in Wearables 2022)

► Show Figures

Figure 1

20 pages, 3296 KiB

Open AccessArticle

Sleep Quality Evaluation Based on Single-Lead Wearable Cardiac Cycle Acquisition Device

by Yang Li, Jianqing Li, Chang Yan, Kejun Dong, Zhiyu Kang, Hongxing Zhang and Chengyu Liu

Sensors 2023, 23(1), 328; https://doi.org/10.3390/s23010328 - 28 Dec 2022

Cited by 1 | Viewed by 2094

Abstract

In clinical conditions, polysomnography (PSG) is regarded as the “golden standard” for detecting sleep disease and offering a reference of objective sleep quality. For healthy adults, scores from sleep questionnaires are more reliable than other methods in obtaining knowledge of subjective sleep quality. [...] Read more.

In clinical conditions, polysomnography (PSG) is regarded as the “golden standard” for detecting sleep disease and offering a reference of objective sleep quality. For healthy adults, scores from sleep questionnaires are more reliable than other methods in obtaining knowledge of subjective sleep quality. In practice, the need to simplify PSG to obtain subjective sleep quality by recording a few channels of physiological signals such as single-lead electrocardiogram (ECG) or photoplethysmography (PPG) signal is still very urgent. This study provided a two-step method to differentiate sleep quality into “good sleep” and “poor sleep” based on the single-lead wearable cardiac cycle data, with the comparison of the subjective sleep questionnaire score. First, heart rate variability (HRV) features and ECG-derived respiration features were extracted to construct a sleep staging model (wakefulness (W), rapid eye movement (REM), light sleep (N1&N2) and deep sleep (N3)) using the multi-classifier fusion method. Then, features extracted from the sleep staging results were used to construct a sleep quality evaluation model, i.e., classifying the sleep quality as good and poor. The accuracy of the sleep staging model, tested on the international public database, was 0.661 and 0.659 in Cardiology Challenge 2018 training database and Sleep Heart Health Study Visit 1 database, respectively. The accuracy of the sleep quality evaluation model was 0.786 for our recording subjects, with an average F₁-score of 0.771. The proposed sleep staging model and sleep quality evaluation model only requires one channel of wearable cardiac cycle signal. It is very easy to transplant to portable devices, which facilitates daily sleep health monitoring. Full article

(This article belongs to the Special Issue Feature Papers in Wearables 2022)

► Show Figures

Figure 1

16 pages, 19886 KiB

Open AccessArticle

Design, Fabrication and Characterisation of Multi-Parameter Optical Sensors Dedicated to E-Skin Applications

by Lionel Fliegans, Joseph Troughton, Valentin Divay, Sylvain Blayac and Marc Ramuz

Sensors 2023, 23(1), 114; https://doi.org/10.3390/s23010114 - 23 Dec 2022

Cited by 1 | Viewed by 1612

Abstract

For many years there has been a strong research interest in soft electronics for artificial skin applications. However, one challenge with stretchable devices is the limited availability of high performance, stretchable, electrical conductors and semiconductors that remain stable under strain. Examples of such [...] Read more.

For many years there has been a strong research interest in soft electronics for artificial skin applications. However, one challenge with stretchable devices is the limited availability of high performance, stretchable, electrical conductors and semiconductors that remain stable under strain. Examples of such electronic skin require excessive amounts of wires to address each sensing element—compression force and strain—in a conventional matrix structure. Here, we present a new process for fabricating artificial skin consisting of an optical waveguide architecture, enabling wide ranging sensitivity to external mechanical compression and strain. The manufacturing process allows design of a fully stretchable polydimethylsiloxane elastomer waveguide with embedded gratings, replicated from low cost DVD-Rs. This optical artificial skin allows the detection of compression forces from 0 to 3.8 N with controllable sensitivity. It also permits monitoring of elongation deformations up to 135%. This type of stretchable optical sensor is highly robust, transparent, and presents a large sensing area while limiting the amount of wires connecting to the sensor. Thus, this optical artificial skin presents far superior mechanical properties compared to current electronic skin. Full article

(This article belongs to the Special Issue Feature Papers in Wearables 2022)

► Show Figures

Figure 1

13 pages, 2639 KiB

Open AccessArticle

Responsiveness of Daily Life Gait Quality Characteristics over One Year in Older Adults Who Experienced a Fall or Engaged in Balance Exercise

by Sabine Schootemeijer, Roel H. A. Weijer, Marco J. M. Hoozemans, Kim Delbaere, Mirjam Pijnappels and Kimberley S. van Schooten

Sensors 2023, 23(1), 101; https://doi.org/10.3390/s23010101 - 22 Dec 2022

Cited by 1 | Viewed by 1569

Abstract

Gait quality characteristics obtained from daily-life accelerometry are clinically relevant for fall risk in older adults but it is unknown whether these characteristics are responsive to changes in gait quality. We aimed to test whether accelerometry-based daily-life gait quality characteristics are reliable and [...] Read more.

Gait quality characteristics obtained from daily-life accelerometry are clinically relevant for fall risk in older adults but it is unknown whether these characteristics are responsive to changes in gait quality. We aimed to test whether accelerometry-based daily-life gait quality characteristics are reliable and responsive to changes over one year in older adults who experienced a fall or an exercise intervention. One-week trunk acceleration data were collected from 522 participants (65–97 years), at baseline and after one year. We calculated median values of walking speed, regularity (sample entropy), stability (logarithmic rate of divergence per stride), and a gait quality composite score, across all 10-s gait epochs derived from one-week gait episodes. Intraclass correlation coefficients (ICC) and limits of agreement (LOA) were determined for 198 participants who did not fall nor participated in an exercise intervention during follow-up. For responsiveness to change, we determined the number of participants who fell (n = 209) or participated in an exercise intervention (n = 115) that showed a change beyond the LOA. ICCs for agreement between baseline and follow-up exceeded 0.70 for all gait quality characteristics except for vertical gait stability (ICC = 0.69, 95% CI [0.62, 0.75]) and walking speed (ICC = 0.68, 95% CI [0.62, 0.74]). Only walking speed, vertical and mediolateral gait stability changed significantly in the exercisers over one year but effect sizes were below 0.2. The characteristic associated with most fallers beyond the LOA was mediolateral sample entropy (4.8% of fallers). For the exercisers, this was gait stability in three directions and the gait quality composite score (2.6% of exercisers). The gait quality characteristics obtained by median values over one week of trunk accelerometry were not responsive to presumed changes in gait quality after a fall or an exercise intervention in older people. This is likely due to large (within subjects) differences in gait behaviour that participants show in daily life. Full article

(This article belongs to the Special Issue Feature Papers in Wearables 2022)

► Show Figures

Figure 1

28 pages, 2014 KiB

Open AccessArticle

Identifying Facilitators, Barriers, and Potential Solutions of Adopting Exoskeletons and Exosuits in Construction Workplaces

by Dilruba Mahmud, Sean T. Bennett, Zhenhua Zhu, Peter G. Adamczyk, Michael Wehner, Dharmaraj Veeramani and Fei Dai

Sensors 2022, 22(24), 9987; https://doi.org/10.3390/s22249987 - 18 Dec 2022

Cited by 10 | Viewed by 2376

Abstract

Exoskeletons and exosuits (collectively termed EXOs) have the potential to reduce the risk of work-related musculoskeletal disorders (WMSDs) by protecting workers from exertion and muscle fatigue due to physically demanding, repetitive, and prolonged work in construction workplaces. However, the use of EXOs in [...] Read more.

Exoskeletons and exosuits (collectively termed EXOs) have the potential to reduce the risk of work-related musculoskeletal disorders (WMSDs) by protecting workers from exertion and muscle fatigue due to physically demanding, repetitive, and prolonged work in construction workplaces. However, the use of EXOs in construction is in its infancy, and much of the knowledge required to drive the acceptance, adoption, and application of this technology is still lacking. The objective of this research is to identify the facilitators, barriers, and corresponding solutions to foster the adoption of EXOs in construction workplaces through a sequential, multistage Delphi approach. Eighteen experts from academia, industry, and government gathered in a workshop to provide insights and exchange opinions regarding facilitators, barriers, and potential solutions from a holistic perspective with respect to business, technology, organization, policy/regulation, ergonomics/safety, and end users (construction-trade professionals). Consensus was reached regarding all these perspectives, including top barriers and potential solution strategies. The outcomes of this study will help the community gain a comprehensive understanding of the potential for EXO use in the construction industry, which may enable the development of a viable roadmap for the evolution of EXO technology and the future of EXO-enabled workers and work in construction workplaces. Full article

(This article belongs to the Special Issue Feature Papers in Wearables 2022)

► Show Figures

Figure 1

16 pages, 9899 KiB

Open AccessArticle

Dual-Band Wearable MIMO Antenna for WiFi Sensing Applications

by Sima Noghanian

Sensors 2022, 22(23), 9257; https://doi.org/10.3390/s22239257 - 28 Nov 2022

Cited by 4 | Viewed by 2118

Abstract

Multiple input multiple output (MIMO) technology combined with orthogonal frequency division multiple access (OFDMA) is an enabling technology used in WiFi 6/6E (IEEE 802.11ax) to increase the throughput. With the addition of WiFi 6/6E and taking advantage of MIMO and OFDMA, many applications [...] Read more.

Multiple input multiple output (MIMO) technology combined with orthogonal frequency division multiple access (OFDMA) is an enabling technology used in WiFi 6/6E (IEEE 802.11ax) to increase the throughput. With the addition of WiFi 6/6E and taking advantage of MIMO and OFDMA, many applications of wearable WiFi can be imagined. For example, WiFi can be used for tracking and fall detection. Wearable devices, such as those used in gaming, vital sign monitoring, and tracking, can also take advantage of wearable MIMO antennas. In this paper, a wearable small dual-band antenna is proposed that can be fabricated on felt or denim substrate. In the proposed antenna, a conductive layer is used as a reflector to improve the gain and reduce the sensitivity of the antenna performance to the body loading effects. The details of the design and its performance in a sample indoor MIMO setting are provided. The MIMO antenna is proposed for WiFi tracking and sensing applications. The performance of the MIMO antenna in an indoor setting is examined. Full article

(This article belongs to the Special Issue Feature Papers in Wearables 2022)

► Show Figures

Figure 1

17 pages, 563 KiB

Open AccessArticle

Assessment of Seasonal Stochastic Local Models for Glucose Prediction without Meal Size Information under Free-Living Conditions

by Francesco Prendin, José-Luis Díez, Simone Del Favero, Giovanni Sparacino, Andrea Facchinetti and Jorge Bondia

Sensors 2022, 22(22), 8682; https://doi.org/10.3390/s22228682 - 10 Nov 2022

Cited by 1 | Viewed by 1292

Abstract

Accurate blood glucose (BG) forecasting is key in diabetes management, as it allows preventive actions to mitigate harmful hypoglycemic/hyperglycemic episodes. Considering the encouraging results obtained by seasonal stochastic models in proof-of-concept studies, this work assesses the methodology in two datasets (open-loop and closed-loop) [...] Read more.

Accurate blood glucose (BG) forecasting is key in diabetes management, as it allows preventive actions to mitigate harmful hypoglycemic/hyperglycemic episodes. Considering the encouraging results obtained by seasonal stochastic models in proof-of-concept studies, this work assesses the methodology in two datasets (open-loop and closed-loop) recorded in free-living conditions. First, similar postprandial glycemic profiles are grouped together with fuzzy C-means clustering. Then, a seasonal stochastic model is identified for each cluster. Finally, real-time BG forecasting is performed by weighting each model’s prediction. The proposed methodology (named C-SARIMA) is compared to other linear and nonlinear black-box methods: autoregressive integrated moving average (ARIMA), its variant with input (ARIMAX), a feed-forward neural network (NN), and its modified version (NN-X) fed by BG, insulin, and carbohydrates (timing and dosing) information for several prediction horizons (PHs). In the open-loop dataset, C-SARIMA grants a median root-mean-squared error (RMSE) of 20.13 mg/dL (PH = 30) and 27.23 mg/dL (PH = 45), not significantly different from ARIMA and NN. Over a longer PH, C-SARIMA achieves an RMSE = 31.96 mg/dL (PH = 60) and RMSE = 33.91 mg/dL (PH = 75), significantly outperforming the ARIMA and NN, without significant differences from the ARIMAX for PH ≥ 45 and the NN-X for PH ≥ 60. Similar results hold on the closed-loop dataset: for PH = 30 and 45 min, the C-SARIMA achieves an RMSE = 21.63 mg/dL and RMSE = 29.67 mg/dL, not significantly different from the ARIMA and NN. On longer PH, the C-SARIMA outperforms the ARIMA for PH > 45 and the NN for PH > 60 without significant differences from the ARIMAX for PH ≥ 45. Although using less input information, the C-SARIMA achieves similar performance to other prediction methods such as the ARIMAX and NN-X and outperforming the CGM-only approaches on PH > 45min. Full article

(This article belongs to the Special Issue Feature Papers in Wearables 2022)

► Show Figures

Figure 1

16 pages, 2948 KiB

Open AccessArticle

How Much Data Is Enough? A Reliable Methodology to Examine Long-Term Wearable Data Acquisition in Gait and Postural Sway

by Brett M. Meyer, Paolo Depetrillo, Jaime Franco, Nicole Donahue, Samantha R. Fox, Aisling O’Leary, Bryn C. Loftness, Reed D. Gurchiek, Maura Buckley, Andrew J. Solomon, Sau Kuen Ng, Nick Cheney, Melissa Ceruolo and Ryan S. McGinnis

Sensors 2022, 22(18), 6982; https://doi.org/10.3390/s22186982 - 15 Sep 2022

Cited by 9 | Viewed by 3774

Abstract

Wearable sensors facilitate the evaluation of gait and balance impairment in the free-living environment, often with observation periods spanning weeks, months, and even years. Data supporting the minimal duration of sensor wear, which is necessary to capture representative variability in impairment measures, are [...] Read more.

Wearable sensors facilitate the evaluation of gait and balance impairment in the free-living environment, often with observation periods spanning weeks, months, and even years. Data supporting the minimal duration of sensor wear, which is necessary to capture representative variability in impairment measures, are needed to balance patient burden, data quality, and study cost. Prior investigations have examined the duration required for resolving a variety of movement variables (e.g., gait speed, sit-to-stand tests), but these studies use differing methodologies and have only examined a small subset of potential measures of gait and balance impairment. Notably, postural sway measures have not yet been considered in these analyses. Here, we propose a three-level framework for examining this problem. Difference testing and intra-class correlations (ICC) are used to examine the agreement in features computed from potential wear durations (levels one and two). The association between features and established patient reported outcomes at each wear duration is also considered (level three) for determining the necessary wear duration. Utilizing wearable accelerometer data continuously collected from 22 persons with multiple sclerosis (PwMS) for 6 weeks, this framework suggests that 2 to 3 days of monitoring may be sufficient to capture most of the variability in gait and sway; however, longer periods (e.g., 3 to 6 days) may be needed to establish strong correlations to patient-reported clinical measures. Regression analysis indicates that the required wear duration depends on both the observation frequency and variability of the measure being considered. This approach provides a framework for evaluating wear duration as one aspect of the comprehensive assessment, which is necessary to ensure that wearable sensor-based methods for capturing gait and balance impairment in the free-living environment are fit for purpose. Full article

(This article belongs to the Special Issue Feature Papers in Wearables 2022)

► Show Figures

Figure 1

13 pages, 1797 KiB

Open AccessArticle

I Can Step Clearly Now, the TENS Is On: Transcutaneous Electric Nerve Stimulation Decreases Sensorimotor Uncertainty during Stepping Movements

by Tyler T. Whittier, Zachary D. Weller and Brett W. Fling

Sensors 2022, 22(14), 5442; https://doi.org/10.3390/s22145442 - 21 Jul 2022

Cited by 2 | Viewed by 1984

Abstract

Transcutaneous electric nerve stimulation (TENS) is a method of electrical stimulation that elicits activity in sensory nerves and leads to improvements in the clinical metrics of mobility. However, the underlying perceptual mechanisms leading to this improvement are unknown. The aim of this study [...] Read more.

Transcutaneous electric nerve stimulation (TENS) is a method of electrical stimulation that elicits activity in sensory nerves and leads to improvements in the clinical metrics of mobility. However, the underlying perceptual mechanisms leading to this improvement are unknown. The aim of this study was to apply a Bayesian inference model to understand how TENS impacts sensorimotor uncertainty during full body stepping movements. Thirty healthy adults visited the lab on two occasions and completed a motor learning protocol in virtual reality (VR) on both visits. Participants were randomly assigned to one of three groups: TENS on first visit only (TN), TENS on second visit only (NT), or a control group where TENS was not applied on either visit (NN). Using methods of Bayesian inference, we calculated the amount of uncertainty in the participants’ center of mass (CoM) position estimates on each visit. We found that groups TN and NT decreased the amount of uncertainty in the CoM position estimates in their second visit while group NN showed no difference. The least amount of uncertainty was seen in the TN group. These results suggest that TENS reduces the amount of uncertainty in sensory information, which may be a cause for the observed benefits with TENS. Full article

(This article belongs to the Special Issue Feature Papers in Wearables 2022)

► Show Figures

Figure 1

9 pages, 2710 KiB

Open AccessArticle

Flexible Pressure Sensor Array with Multi-Channel Wireless Readout Chip

by Haohan Wangxu, Liangjian Lyu, Hengchang Bi and Xing Wu

Sensors 2022, 22(10), 3934; https://doi.org/10.3390/s22103934 - 23 May 2022

Viewed by 2763

Abstract

Flexible sensor arrays are widely used for wearable physiological signal recording applications. A high density sensor array requires the signal readout to be compatible with multiple channels. This paper presents a highly-integrated remote health monitoring system integrating a flexible pressure sensor array with [...] Read more.

Flexible sensor arrays are widely used for wearable physiological signal recording applications. A high density sensor array requires the signal readout to be compatible with multiple channels. This paper presents a highly-integrated remote health monitoring system integrating a flexible pressure sensor array with a multi-channel wireless readout chip. The custom-designed chip features 64 voltage readout channels, a power management unit, and a wireless transceiver. The whole chip fabricated in a 65 nm complementary metal-oxide-semiconductor (CMOS) process occupies 3.7 × 3.7 mm², and the core blocks consume 2.3 mW from a 1 V supply in the wireless recording mode. The proposed multi-channel system is validated by measuring the ballistocardiogram (BCG) and pulse wave, which paves the way for future portable remote human physiological signals monitoring devices. Full article

(This article belongs to the Special Issue Feature Papers in Wearables 2022)

► Show Figures

Figure 1

30 pages, 2259 KiB

Open AccessArticle

“Listen to Your Immune System When It’s Calling for You”: Monitoring Autoimmune Diseases Using the iShU App

by Cláudia Ortet and Liliana Vale Costa

Sensors 2022, 22(10), 3834; https://doi.org/10.3390/s22103834 - 18 May 2022

Cited by 1 | Viewed by 4448

Abstract

The immune system plays a key role in protecting living beings against bacteria, viruses, and fungi, among other pathogens, which may be harmful and represent a threat to our own health. However, for reasons that are not fully understood, in some people this [...] Read more.

The immune system plays a key role in protecting living beings against bacteria, viruses, and fungi, among other pathogens, which may be harmful and represent a threat to our own health. However, for reasons that are not fully understood, in some people this protective mechanism accidentally attacks the organs and tissues, thus causing inflammation and leads to the development of autoimmune diseases. Remote monitoring of human health involves the use of sensor network technology as a means of capturing patient data, and wearable devices, such as smartwatches, have lately been considered good collectors of biofeedback data, owing to their easy connectivity with a mHealth system. Moreover, the use of gamification may encourage the frequent usage of such devices and behavior changes to improve self-care for autoimmune diseases. This study reports on the use of wearable sensors for inflammation surveillance and autoimmune disease management based on a literature search and evaluation of an app prototype with fifteen stakeholders, in which eight participants were diagnosed with autoimmune or inflammatory diseases and four were healthcare professionals. Of these, six were experts in human–computer interaction to assess critical aspects of user experience. The developed prototype allows the monitoring of autoimmune diseases in pre-, during-, and post-inflammatory crises, meeting the personal needs of people with this health condition. The findings suggest that the proposed prototype—iShU—achieves its purpose and the overall experience may serve as a foundation for designing inflammation surveillance and autoimmune disease management monitoring solutions. Full article

(This article belongs to the Special Issue Feature Papers in Wearables 2022)

► Show Figures

Figure 1

Review

Jump to: Research

31 pages, 7945 KiB

Open AccessReview

State-of-the-Art Review on Wearable Obstacle Detection Systems Developed for Assistive Technologies and Footwear

by Anna M. Joseph, Azadeh Kian and Rezaul Begg

Sensors 2023, 23(5), 2802; https://doi.org/10.3390/s23052802 - 03 Mar 2023

Cited by 4 | Viewed by 4500

Abstract

Walking independently is essential to maintaining our quality of life but safe locomotion depends on perceiving hazards in the everyday environment. To address this problem, there is an increasing focus on developing assistive technologies that can alert the user to the risk destabilizing [...] Read more.

Walking independently is essential to maintaining our quality of life but safe locomotion depends on perceiving hazards in the everyday environment. To address this problem, there is an increasing focus on developing assistive technologies that can alert the user to the risk destabilizing foot contact with either the ground or obstacles, leading to a fall. Shoe-mounted sensor systems designed to monitor foot-obstacle interaction are being employed to identify tripping risk and provide corrective feedback. Advances in smart wearable technologies, integrating motion sensors with machine learning algorithms, has led to developments in shoe-mounted obstacle detection. The focus of this review is gait-assisting wearable sensors and hazard detection for pedestrians. This literature represents a research front that is critically important in paving the way towards practical, low-cost, wearable devices that can make walking safer and reduce the increasing financial and human costs of fall injuries. Full article

(This article belongs to the Special Issue Feature Papers in Wearables 2022)

► Show Figures

Figure 1

25 pages, 3282 KiB

Open AccessReview

Markerless Radio Frequency Indoor Monitoring for Telemedicine: Gait Analysis, Indoor Positioning, Fall Detection, Tremor Analysis, Vital Signs and Sleep Monitoring

by Lazzaro di Biase, Pasquale Maria Pecoraro, Giovanni Pecoraro, Maria Letizia Caminiti and Vincenzo Di Lazzaro

Sensors 2022, 22(21), 8486; https://doi.org/10.3390/s22218486 - 04 Nov 2022

Cited by 7 | Viewed by 2639

Abstract

Quantitative indoor monitoring, in a low-invasive and accurate way, is still an unmet need in clinical practice. Indoor environments are more challenging than outdoor environments, and are where patients experience difficulty in performing activities of daily living (ADLs). In line with the recent [...] Read more.

Quantitative indoor monitoring, in a low-invasive and accurate way, is still an unmet need in clinical practice. Indoor environments are more challenging than outdoor environments, and are where patients experience difficulty in performing activities of daily living (ADLs). In line with the recent trends of telemedicine, there is an ongoing positive impulse in moving medical assistance and management from hospitals to home settings. Different technologies have been proposed for indoor monitoring over the past decades, with different degrees of invasiveness, complexity, and capabilities in full-body monitoring. The major classes of devices proposed are inertial-based sensors (IMU), vision-based devices, and geomagnetic and radiofrequency (RF) based sensors. In recent years, among all available technologies, there has been an increasing interest in using RF-based technology because it can provide a more accurate and reliable method of tracking patients’ movements compared to other methods, such as camera-based systems or wearable sensors. Indeed, RF technology compared to the other two techniques has higher compliance, low energy consumption, does not need to be worn, is less susceptible to noise, is not affected by lighting or other physical obstacles, has a high temporal resolution without a limited angle of view, and fewer privacy issues. The aim of the present narrative review was to describe the potential applications of RF-based indoor monitoring techniques and highlight their differences compared to other monitoring technologies. Full article

(This article belongs to the Special Issue Feature Papers in Wearables 2022)

► Show Figures

Figure 1

38 pages, 3939 KiB

Open AccessReview

Precordial Vibrations: A Review of Wearable Systems, Signal Processing Techniques, and Main Applications

by Francesca Santucci, Daniela Lo Presti, Carlo Massaroni, Emiliano Schena and Roberto Setola

Sensors 2022, 22(15), 5805; https://doi.org/10.3390/s22155805 - 03 Aug 2022

Cited by 14 | Viewed by 3377

Abstract

Recently, the ever-growing interest in the continuous monitoring of heart function in out-of-laboratory settings for an early diagnosis of cardiovascular diseases has led to the investigation of innovative methods for cardiac monitoring. Among others, wearables recording seismic waves induced on the chest surface [...] Read more.

Recently, the ever-growing interest in the continuous monitoring of heart function in out-of-laboratory settings for an early diagnosis of cardiovascular diseases has led to the investigation of innovative methods for cardiac monitoring. Among others, wearables recording seismic waves induced on the chest surface by the mechanical activity of the heart are becoming popular. For what concerns wearable-based methods, cardiac vibrations can be recorded from the thorax in the form of acceleration, angular velocity, and/or displacement by means of accelerometers, gyroscopes, and fiber optic sensors, respectively. The present paper reviews the currently available wearables for measuring precordial vibrations. The focus is on sensor technology and signal processing techniques for the extraction of the parameters of interest. Lastly, the explored application scenarios and experimental protocols with the relative influencing factors are discussed for each technique. The goal is to delve into these three fundamental aspects (i.e., wearable system, signal processing, and application scenario), which are mutually interrelated, to give a holistic view of the whole process, beyond the sensor aspect alone. The reader can gain a more complete picture of this context without disregarding any of these 3 aspects. Full article

(This article belongs to the Special Issue Feature Papers in Wearables 2022)

► Show Figures

Figure 1

28 pages, 1146 KiB

Open AccessReview

A Systematic Survey of Research Trends in Technology Usage for Parkinson’s Disease

by Ranadeep Deb, Sizhe An, Ganapati Bhat, Holly Shill and Umit Y. Ogras

Sensors 2022, 22(15), 5491; https://doi.org/10.3390/s22155491 - 23 Jul 2022

Cited by 12 | Viewed by 2482

Abstract

Parkinson’s disease (PD) is a neurological disorder with complicated and disabling motor and non-motor symptoms. The complexity of PD pathology is amplified due to its dependency on patient diaries and the neurologist’s subjective assessment of clinical scales. A significant amount of recent research [...] Read more.

Parkinson’s disease (PD) is a neurological disorder with complicated and disabling motor and non-motor symptoms. The complexity of PD pathology is amplified due to its dependency on patient diaries and the neurologist’s subjective assessment of clinical scales. A significant amount of recent research has explored new cost-effective and subjective assessment methods pertaining to PD symptoms to address this challenge. This article analyzes the application areas and use of mobile and wearable technology in PD research using the PRISMA methodology. Based on the published papers, we identify four significant fields of research: diagnosis, prognosis and monitoring, predicting response to treatment, and rehabilitation. Between January 2008 and December 2021, 31,718 articles were published in four databases: PubMed Central, Science Direct, IEEE Xplore, and MDPI. After removing unrelated articles, duplicate entries, non-English publications, and other articles that did not fulfill the selection criteria, we manually investigated 1559 articles in this review. Most of the articles (45%) were published during a recent four-year stretch (2018–2021), and 19% of the articles were published in 2021 alone. This trend reflects the research community’s growing interest in assessing PD with wearable devices, particularly in the last four years of the period under study. We conclude that there is a substantial and steady growth in the use of mobile technology in the PD contexts. We share our automated script and the detailed results with the public, making the review reproducible for future publications. Full article

(This article belongs to the Special Issue Feature Papers in Wearables 2022)

► Show Figures

Figure 1

Journal Menu

Journal Browser

Feature Papers in Wearables 2022

Share This Special Issue

Special Issue Editor

Special Issue Information

Published Papers (17 papers)

Research

Review

Further Information

Guidelines

MDPI Initiatives

Follow MDPI