Occupational Risk Prevention through Smartwatches: Precision and Uncertainty Effects of the Built-In Accelerometer

Sigcha, Luis; Pavón, Ignacio; Arezes, Pedro; Costa, Nélson; Arcas, Guillermo de; López, Juan M.

doi:10.3390/s18113805

Cited by 19 publications

(16 citation statements)

References 69 publications

(102 reference statements)

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“…An in-depth discussion of the IoT-ACRES hardware and software is not in the scope of this paper. The authors of [52] evaluate the use of wearable technologies and focus on smartwatches as these devices are becoming more common every day. After testing the smartwatches, the authors concluded that while they are affordable technology, they were not reliable devices for safety data acquisition.…”

Section: Methodsmentioning

confidence: 99%

Leading Indicators—A Conceptual IoT-Based Framework to Produce Active Leading Indicators for Construction Safety

Costin

Wehle

Adibfar

2019

Safety

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Active leading indicators (ALIs) have the potential to identify safety hazards and prompt immediate actions to prevent incidents. Currently, there is a major gap in research that incorporates a fully automated ALI system because implementation has been hindered by a lack of established industry thresholds of measurable performance that would trigger an actionable response. Therefore, this paper addresses this gap by presenting a new method that utilizes the Internet of Things (IoT) to collect quantifiable data which can trigger an actionable response in real time based on established thresholds. This novel method integrates the Construction Industry Institute (CII) active leading indicator framework with a prototype IoT-based system. Significantly, the ALI provides the physical–virtual feedback loop, which is an essential aspect of the IoT system because it provides real-time feedback to both the users and systems. This paper also identifies potential inputs to the ALI framework from emerging IoT-enabled systems. A case study was presented to initially validate the IoT-based ALI framework. Bluetooth-enabled heart rate monitors were issued to workers in a hazardous and critical mining construction site. The ALIs that were recorded included heart rate and body temperature. Thresholds were established that alerted the monitoring safety staff when a worker exhibited potentially unsafe conditions. The results of the study demonstrated the feasibility of the system. Additionally, other results included worker resistance; non-disclosing of medical conditions, and limitations for IoT connectivity.

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Section: Methodsmentioning

confidence: 99%

Leading Indicators—A Conceptual IoT-Based Framework to Produce Active Leading Indicators for Construction Safety

Costin

Wehle

Adibfar

2019

Safety

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“…The smartwatch used for the data collection was previously analyzed to identify its frequency response by using a methodology described in a previous work [ 85 ]. The analysis employs an adapted version of the calibration method by comparison with a reference accelerometer.…”

Section: Methodsmentioning

confidence: 99%

“…According to the methodology established in [ 85 ], an error band has been considered for amplitude and frequency linearity according to the specifications of the International Organization for Standardization (ISO) 8041:2005 [ 86 ], in which values with a deviation greater than ±6% are considered linearity errors.…”

Section: Methodsmentioning

confidence: 99%

Automatic Resting Tremor Assessment in Parkinson’s Disease Using Smartwatches and Multitask Convolutional Neural Networks

Sigcha

Pavón

Costa

et al. 2021

Sensors

Self Cite

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Resting tremor in Parkinson’s disease (PD) is one of the most distinctive motor symptoms. Appropriate symptom monitoring can help to improve management and medical treatments and improve the patients’ quality of life. Currently, tremor is evaluated by physical examinations during clinical appointments; however, this method could be subjective and does not represent the full spectrum of the symptom in the patients’ daily lives. In recent years, sensor-based systems have been used to obtain objective information about the disease. However, most of these systems require the use of multiple devices, which makes it difficult to use them in an ambulatory setting. This paper presents a novel approach to evaluate the amplitude and constancy of resting tremor using triaxial accelerometers from consumer smartwatches and multitask classification models. These approaches are used to develop a system for an automated and accurate symptom assessment without interfering with the patients’ daily lives. Results show a high agreement between the amplitude and constancy measurements obtained from the smartwatch in comparison with those obtained in a clinical assessment. This indicates that consumer smartwatches in combination with multitask convolutional neural networks are suitable for providing accurate and relevant information about tremor in patients in the early stages of the disease, which can contribute to the improvement of PD clinical evaluation, early detection of the disease, and continuous monitoring.

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“…The authors believed that there is great potential for the application of their proposed wearable insole pressure system, as it has both practical uses and economic benefits due to the usage of IoT technologies including sensors, vision-based technologies, and wireless communication. Sigcha et al [ 9 ] believed that wearable technology, such as microelectronic mechanical systems (MEMS), accelerometers, and smartwatches had witnessed tremendous evolution in the domain of safety research. Among these, smartwatches are easy to use and have broad applicability, e.g., integrating an accelerometer with a smartwatch for precise motion detection, and considering any uncertainty effects in the occupational risk assessment.…”

Section: Literature Reviewmentioning

confidence: 99%

Investigating the Barriers to Applying the Internet-of-Things-Based Technologies to Construction Site Safety Management

Tabatabaee

Mohandes

Ahmed

et al. 2022

IJERPH

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The utilization of Internet-of-Things (IoT)-based technologies in the construction industry has recently grabbed the attention of numerous researchers and practitioners. Despite the improvements made to automate this industry using IoT-based technologies, there are several barriers to the further utilization of these leading-edge technologies. A review of the literature revealed that it lacks research focusing on the obstacles to the application of these technologies in Construction Site Safety Management (CSSM). Accordingly, the aim of this research was to identify and analyze the barriers impeding the use of such technologies in the CSSM context. To this end, initially, the extant literature was reviewed extensively and nine experts were interviewed, which led to the identification of 18 barriers. Then, the fuzzy Delphi method (FDM) was used to calculate the importance weights of the identified barriers and prioritize them through the lenses of competent experts in Hong Kong. Following this, the findings were validated using semi-structured interviews. The findings showed that the barriers related to “productivity reduction due to wearable sensors”, “the need for technical training”, and “the need for continuous monitoring” were the most significant, while “limitations on hardware and software and lack of standardization in efforts,” “the need for proper light for smooth functionality”, and “safety hazards” were the least important barriers. The obtained findings not only give new insight to academics, but also provide practical guidelines for the stakeholders at the forefront by enabling them to focus on the key barriers to the implementation of IoT-based technologies in CSSM.

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Occupational Risk Prevention through Smartwatches: Precision and Uncertainty Effects of the Built-In Accelerometer

Cited by 19 publications

References 69 publications

Leading Indicators—A Conceptual IoT-Based Framework to Produce Active Leading Indicators for Construction Safety

Leading Indicators—A Conceptual IoT-Based Framework to Produce Active Leading Indicators for Construction Safety

Automatic Resting Tremor Assessment in Parkinson’s Disease Using Smartwatches and Multitask Convolutional Neural Networks

Investigating the Barriers to Applying the Internet-of-Things-Based Technologies to Construction Site Safety Management

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