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The Significance of Witness Sensors for Mass Casualty Incidents and Epidemic Outbreaks

The Significance of Witness Sensors for Mass Casualty Incidents and Epidemic Outbreaks

In this research, we introduce a novel concept that is based on the two elements of “witness” and “sensor” to address this challenge. Witness sensors respond in a similar way to physical sensors, since they interact and report in real time. Physical sensors are calibrated and modulated prior to any operation so that only the signals from qualified physical sensors are accepted.

Chih-Long Pan, Chih-Hao Lin, Yan-Ren Lin, Hsin-Yu Wen, Jet-Chau Wen

J Med Internet Res 2018;20(2):e39

Identifying Gravity-Related Artifacts on Ballistocardiography Signals by Comparing Weightlessness and Normal Gravity Recordings (ARTIFACTS): Protocol for an Observational Study

Identifying Gravity-Related Artifacts on Ballistocardiography Signals by Comparing Weightlessness and Normal Gravity Recordings (ARTIFACTS): Protocol for an Observational Study

Wearable smart sensor technology necessitates real-life measurements to make the results usable for individualized and tailored care [1]. Ballistocardiography (BCG) and seismocardiography (SCG) are generally suitable for cardiovascular diagnostics (heart rhythm, functionality, and vascular status) and the extraction of parameters such as blood pressure or respiration at a relatively low cost and with a low-risk potential.

Urs-Vito Albrecht, Annabelle Mielitz, Kazi Mohammad Abidur Rahman, Ulf Kulau

JMIR Res Protoc 2024;13:e63306

Using In-Shoe Inertial Measurement Unit Sensors to Understand Daily-Life Gait Characteristics in Patients With Distal Radius Fractures During 6 Months of Recovery: Cross-Sectional Study

Using In-Shoe Inertial Measurement Unit Sensors to Understand Daily-Life Gait Characteristics in Patients With Distal Radius Fractures During 6 Months of Recovery: Cross-Sectional Study

The IMU sensor in the dedicated insole was placed at the foot arch, and the x-, y-, and z-axes of the IMUs were set along the mediolateral, anteroposterior, and vertical directions, respectively. When a person wearing these sensors walks in a stable straight line over 3 gait cycles between 5 AM and 10 PM, the in-shoe IMU sensor detects that the person is walking based on acceleration in the anteroposterior direction and saves the IMU signals of the next 3 gait cycles as 1 gait measurement [21].

Akiko Yamamoto, Eriku Yamada, Takuya Ibara, Fumiyuki Nihey, Takuma Inai, Kazuya Tsukamoto, Tomohiko Waki, Toshitaka Yoshii, Yoshiyuki Kobayashi, Kentaro Nakahara, Koji Fujita

JMIR Mhealth Uhealth 2024;12:e55178

Reliability and Accuracy of the Fitbit Charge 4 Photoplethysmography Heart Rate Sensor in Ecological Conditions: Validation Study

Reliability and Accuracy of the Fitbit Charge 4 Photoplethysmography Heart Rate Sensor in Ecological Conditions: Validation Study

However, even if the concept remains similar, connected watches usually come with a green light PPG sensor for its ability to reduce motion artifacts, contrary to the red ones commonly used in the medical field for blood oxygen saturation evaluation [8,9]. The reason for this is that the deeper the light penetrates the tissue (eg, red wavelength), the more the pulse wave is affected by limb movements [10,11].

Maxime Ceugniez, Hervé Devanne, Eric Hermand

JMIR Mhealth Uhealth 2025;13:e54871

Framework for Ranking Machine Learning Predictions of Limited, Multimodal, and Longitudinal Behavioral Passive Sensing Data: Combining User-Agnostic and Personalized Modeling

Framework for Ranking Machine Learning Predictions of Limited, Multimodal, and Longitudinal Behavioral Passive Sensing Data: Combining User-Agnostic and Personalized Modeling

The second challenge commonly faced when tackling passive sensor data is that of sparsity or noise. This challenge arises due to signal inconsistencies and noise in sensor data collection because of software issues, data sync, or hardware problems. Discussions of sparsity and the negative effect it has on modeling have been previously documented [7,18-20].

Tahsin Mullick, Sam Shaaban, Ana Radovic, Afsaneh Doryab

JMIR AI 2024;3:e47805

Continuous Critical Respiratory Parameter Measurements Using a Single Low-Cost Relative Humidity Sensor: Evaluation Study

Continuous Critical Respiratory Parameter Measurements Using a Single Low-Cost Relative Humidity Sensor: Evaluation Study

In this work, we present a simple and inexpensive sensor platform that can be used to quantify pulmonary inspiration, expiration, and lung volumes. Our device uses a relative humidity sensor (RHS) to detect breathing and calculate tidal volumes (TVs), expiratory reserve volumes (ERVs), and inspiratory reserve volumes (IRVs) [19].

Fabrice Vaussenat, Abhiroop Bhattacharya, Julie Payette, Jaime A Benavides-Guerrero, Alexandre Perrotton, Luis Felipe Gerlein, Sylvain G Cloutier

JMIR Biomed Eng 2023;8:e47146

Nurses’ Roles in mHealth App Development: Scoping Review

Nurses’ Roles in mHealth App Development: Scoping Review

As apps become more sophisticated with sensor technology and interoperable with clinical decision support systems, adopting m Health standards to support more advanced functions and features will be essential. Standards for m Health app development exist but are rarely described in the literature, with the vast majority of publications lacking a description of technical standards or content standards.

Caitlin J Bakker, Tami H Wyatt, Melissa CS Breth, Grace Gao, Lisa M Janeway, Mikyoung A Lee, Christie L Martin, Victoria L Tiase

JMIR Nursing 2023;6:e46058

Passive Mobile Self-tracking of Mental Health by Veterans With Serious Mental Illness: Protocol for a User-Centered Design and Prospective Cohort Study

Passive Mobile Self-tracking of Mental Health by Veterans With Serious Mental Illness: Protocol for a User-Centered Design and Prospective Cohort Study

Passive mobile sensing can use sensor and phone data from mobile devices to detect health-related behaviors (eg, exercise, social interaction, sleep), which may allow one to detect changes in symptoms and functioning across various behavioral domains like activity, sociability, and sleep [10,11]. These associations have been found in patients with SMI.

Alexander S Young, Abigail Choi, Shay Cannedy, Lauren Hoffmann, Lionel Levine, Li-Jung Liang, Melissa Medich, Rebecca Oberman, Tanya T Olmos-Ochoa

JMIR Res Protoc 2022;11(8):e39010

SciKit Digital Health: Python Package for Streamlined Wearable Inertial Sensor Data Processing

SciKit Digital Health: Python Package for Streamlined Wearable Inertial Sensor Data Processing

Sleep and physical activity monitoring, which typically use a wrist-based sensor, are also among extensively researched areas [11-18]. Sleep and physical activity research have been aided by the availability of an open-source, freely available code package, GGIR [19]. GGIR is a collection of algorithms for activity and sleep research, written in R, and includes code to ingest, calibrate, and detect sleep and activity level from raw acceleration data.

Lukas Adamowicz, Yiorgos Christakis, Matthew D Czech, Tomasz Adamusiak

JMIR Mhealth Uhealth 2022;10(4):e36762

Integrating Behavior of Children with Profound Intellectual, Multiple, or Severe Motor Disabilities With Location and Environment Data Sensors for Independent Communication and Mobility: App Development and Pilot Testing

Integrating Behavior of Children with Profound Intellectual, Multiple, or Severe Motor Disabilities With Location and Environment Data Sensors for Independent Communication and Mobility: App Development and Pilot Testing

The Io T Smart Module Sensor Network Module Evaluation kit consists of a multifunction Bluetooth sensor (ALPS Sensor; Figure 2, c) module (Mouser and manufacturer number: 688-UGWZ3 AA001 A Sensor Network Kit W/BLE Mod Sensors) developed by ALPS Alpine [34]. It has a 2.4 GHz frequency, operates with a 3.3 volt supply, and has –93 d Bm Bluetooth receiver sensitivity [34].

Von Ralph Dane Marquez Marquez Herbuela, Tomonori Karita, Yoshiya Furukawa, Yoshinori Wada, Yoshihiro Yagi, Shuichiro Senba, Eiko Onishi, Tatsuo Saeki

JMIR Rehabil Assist Technol 2021;8(2):e28020

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