1,720,963 research outputs found
Wearable Sensors for Supporting Diagnosis, Prognosis, and Monitoring of Neurodegenerative Diseases
Full text linkFostering Human Activity Recognition Workflows: An Open-Source Baseline Framework
No full textThe application of machine and deep learning algorithms in Human Activity Recognition (HAR) has shown great potential for monitoring various professional and daily life activities, benefiting different research areas such as healthcare, well-being and industrial automation. HAR can enable the development of various services and applications to empower technical performance and enable risk prevention in working places, to support education and training, and, more in general, to monitor the biopsychosocial status of people. However, we still lack a baseline framework for easily implementing the data processing pipeline that must be designed to setup and configure HAR workflows. This makes challenging to estimate the effectiveness, efficiency, and the overall quality of HAR solutions, thus hindering the comparison among different approaches. This also increases the likelihood that researchers introduce errors, which negatively affect the accuracy of the obtained results. To fill in the gap, this paper introduces B-HAR, an open-source framework to automatically implement baseline HAR workflows
Non-invasive monitoring of Alzheimer's patients through WiFi channel state information
No full textThe design of noninvasive systems for monitoring people's activities is becoming of central interest in recent years. Such systems are essential for those affected by diseases that modify their cognitive status and are not collaborative in using wearable or interactive systems (e.g., mobile apps to communicate). This is particularly true regarding neurodegenerative diseases that involve memory loss, cognitive decline, communication difficulties, behavioral changes, loss of independence, and physical complications. In response to the need of healthcare structures and caregivers to monitor this category of people during their in-home daily life, this paper proposes a nonintrusive system capable of detecting whether or not a person is in his/her room and if he/she is lying on the bed. Checking these conditions is of utmost importance, in particular, during the night to support the monitoring activity of caregivers and social-health operators taking care of people with Dementia and Alzheimer's disease. The proposed system exploits WiFi's Channel State Information (CSI) gathered by common access points installed in the room. CSI data are then used to train a Convolutional Neural Network (CNN) and a fine-tuning technique is applied to increase the generalization capabilities of the CNN model on new environments. In our experimental analysis, we trained the CNN model by collecting CSI data in four different rooms, from two subjects performing three distinct activities. Promising results have been achieved (accuracy > 97.5%) in recognizing the target activities
Towards the automatic data annotation for human activity recognition based on wearables and BLE beacons
No full textIn machine learning, the data annotation process is an essential, but error-prone and time-consuming manual activity, which associates metadata to the samples of a dataset. In the context of Human Activity Recognition (HAR) this generally reflects in a human watching the video recordings of the activities carried out by the target user to assign a label to each video frame. The label can refer, for example, to the nature of the performed activity, or to the time series collected through sensors worn by the user or present in the environment. This paper deals with the automation of the data annotation process in the HAR context by presenting a methodology that (i) maps Bluetooth Low Energy (BLE) beacons distributed in the environment to the locations where a human typically performs activities like eating, cooking, working, and resting, and (ii) associates the data collected by sensors embedded in the smartwatch worn by the user (i.e., acceleration, angular velocity, and magnetometer) to the nearest BLE beacon. In this way, data gathered through the smartwatch are automatically annotated with the human activity associated to the nearest beacon
Edge-based freezing of gait recognition in Parkinson's disease
Full text linkFreezing of gait (FoG) stands as one of the most debilitating symptoms of Parkinson's disease (PD), occurring in more than half of patients with advanced PD. This condition manifests as a sudden blockage, significantly reducing the patients’ quality of life. To improve gait and ameliorate FoG, cueing strategies involving audio, visual, or tactile stimulation have been evaluated. In particular, on-demand systems that can automatically detect FoG and administer cueing have emerged as promising solutions. In response, several wearable sensors and machine learning-based approaches have been proposed for accurate FoG recognition. However, existing techniques suffer from several critical challenges, notably suboptimal performance, and limitations for real-time operation and edge deployment. Addressing these issues, this study presents a groundbreaking advancement in real-time edge-based FoG recognition utilizing convolutional neural networks (CNN). We designed an optimized model, rigorously evaluating it across 62 PD patients using a cutting-edge reference dataset, achieving an F1-score of 92% and an area under the curve of 0.97. Further testing on an external dataset resulted in consistent detection performance, while a lower specificity was observed. The CNN implementation on a cost-effective processing device resulted in a 1 ms inference time and required only 6.3 KB of random access memory (RAM) and 37.8 KB of Flash memory, meeting real-time demands and enhancing clinical applicability
Estimating Indoor Occupancy Through Low-Cost BLE Devices
Full text linkDetecting the presence of persons and estimating their quantity in an indoor environment has grown in importance recently. For example, the information if a room is unoccupied can be used for automatically switching off the light, air conditioning, and ventilation, thereby saving significant amounts of energy in public buildings. Most existing solutions rely on dedicated hardware installations, which involve presence sensors, video cameras, and carbon dioxide sensors. Unfortunately, such approaches are costly, subject to privacy concerns, have high computational requirements, and lack ubiquitousness. The work presented in this article addresses these limitations by proposing a low-cost occupancy detection system. Our approach builds upon detecting variations in Bluetooth Low Energy (BLE) signals related to the presence of humans. The effectiveness of this approach is evaluated by performing comprehensive tests on five different datasets. We apply several pattern recognition models and compare our methodology with systems building upon IEEE 802.11 (WiFi). On average, in multifarious environments, we can correctly classify the occupancy with an accuracy of 97.97%. When estimating the number of people in a room, on average, the estimated number of subjects differs from the actual one by 0.32 persons. We conclude that our system's performance is comparable to that of existing ones based on WiFi, while significantly reducing cost and installation effort. Hence, our approach makes occupancy detection practical for real-world deployments
An indoor localization system to detect areas causing the freezing of gait in Parkinsonians
No full textA low-cost BLE-based distance estimation, occupancy detection and counting system
No full textThis article presents a low-cost system for distance estimation, occupancy counting, and presence detection based on Bluetooth Low Energy radio signal variation patterns that mitigates the limitation of existing approaches related to economic cost, privacy concerns, computational requirements, and lack of ubiquitousness. To explore the approach effectiveness, exhaustive tests have been carried out on four different datasets by exploiting several pattern recognition models
Cueing Technologies in Parkinson's Disease: A Systematic Review
Full text linkParkinson's disease (PD) is a progressive neurodegenerative disorder characterized by motor and non-motor symptoms that significantly impact daily life. Wearable cueing technologies have emerged as promising interventions to alleviate these symptoms by providing external sensory stimuli to enhance movement, cognition, and overall function. However, the landscape of wearable cueing methodologies in PD clinical applications remains fragmented. In this systematic review, we analyze the effectiveness of wearable cueing technologies designed for people with PD (PwPD), focusing on their impact on motor and non-motor symptoms. Following PRISMA guidelines, we conducted a comprehensive literature search, identifying 1,640 studies, of which 39 met the inclusion criteria. These studies explored various cueing modalities, including auditory, visual, haptic, and multimodal approaches, tested in clinical settings. Our findings indicate that wearable cueing technologies show significant potential in mitigating motor symptoms, such as freezing of gait, bradykinesia, and postural instability. Although our review framework considered both motor and non-motor symptoms, none of the included studies explicitly addressed non-motor impairments (e.g., cognitive, affective, or sleep-related symptoms), highlighting an unmet research need in this area and confirming the current technological focus on motor rehabilitation. However, the effectiveness of these interventions varies depending on the cueing modality, patient-specific factors, and study design. Despite promising results, the heterogeneity in study protocols, sample sizes, and outcome measures poses challenges in establishing standardized conclusions. This review underscores the growing role of wearable cueing technologies in PD management and highlights the need for high-quality, standardized clinical trials to refine device design, optimize cueing parameters, and integrate these solutions into personalized treatment strategies. Our findings provide a foundation for future research and the development of evidence-based wearable interventions to enhance the quality of life for PwPD
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