Outdoor multimodal system based on smartphone for health monitoring and incident detection

Nedjai-Merrouche, Ikram; Saadia, Nadia; Ramdane-Chérif, Amar; Makhlouf, Amina

doi:10.1007/s12652-020-02880-5

Cited by 8 publications

(4 citation statements)

References 47 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…With the development of Smartphones integrating accelerometers, gyroscopes, cameras and communication tools, these are also used in several research works [17,18]. In the study [19], the authors propose a fall detection system based on a Smartphone's accelerometer data from two publicly available databases.…”

Section: Smartphone Based Systemsmentioning

confidence: 99%

Multimodal System of Ambient Assistance Services for Human Activity Monitoring

Boudouane,

Makhlouf,

Djelal

et al. 2023

JESA

View full text Add to dashboard Cite

The global demographic has seen a significant surge in the population aged 65 or over in recent years, a trend projected to accelerate in the coming decades. This elderly demographic is progressively losing autonomy, becoming increasingly susceptible to domestic accidents such as falls and heart rhythm abnormalities. To address this, this article introduces a multimodal system designed for continuous monitoring of elderly or disabled individuals within their homes. The developed architecture hinges on a fusion system, integrating signals from acceleration, heart rate, and presence sensors to generate ambient services. These services enable simultaneous detection of heart rate irregularities and falls, as well as tracking the individual's location within their home. Our methodology proposes a conditional fusion, employing IF THEN ELSE rules to produce outputs correlated to the presence or absence of one or more critical situations. This strategy amplifies the accuracy of moving object estimation, particularly during activities of daily living (ADLs), and ensures synchronized assistance services. An emergency service is introduced to classify the urgency and initiate the appropriate action. Validation of the proposed architecture and performance analysis were conducted using the CPNTools tool. Experimental test-based results demonstrated a 78.33% accuracy in the fall detection service. Heart disorder detection service tests confirmed 100% success rate in detecting tachycardia, while the location service demonstrated a sensitivity of 90%.

show abstract

Section: Smartphone Based Systemsmentioning

confidence: 99%

Multimodal System of Ambient Assistance Services for Human Activity Monitoring

Boudouane,

Makhlouf,

Djelal

et al. 2023

JESA

View full text Add to dashboard Cite

show abstract

“…Nedjai-Merrouche et al proposed outdoor multimodal system based on smartphone for health monitoring and incident detection (Nedjai-Merrouche et al 2021). They showed that the proposed solution can detect falls from walking or standing with 97.77% precision (Table 1).…”

Section: Related Workmentioning

confidence: 99%

Development of a mobile health infrastructure for non-communicable diseases using design science research method: a case study

Sumarsono

Sakkinah

Permanasari

et al. 2022

J Ambient Intell Human Comput

View full text Add to dashboard Cite

In this paper, we share our experience in designing and developing a mobile health (mHealth) infrastructure for non-communicable diseases (NCD) in rural areas in Indonesia called NusaHealth. The NusaHealth project builds a digital healthcare infrastructure involving universities, healthcare providers, and communities in an mHealth approach that puts patients at the center of health care. The Design Science Research Methodology (DSRM) framework was adopted in designing the mHealth application. The infrastructure to connect the mobile device network with the hospital information system was also developed. Our finding indicates that designing and developing an mHealth solution for rural areas in developing countries needs comprehensive approach and the implementation process should involve related partners and stakeholders. The NusaHealth was implemented in rural areas in Yogyakarta province in Indonesia. While Yogyakarta province successfully implemented the NusaHealth in rural areas, different activities need to be conducted to enhance community health by developing a formal mHealth system supported by local health district offices’ policies and regulations.

show abstract

“…It was noted that a fall typically results in a substantial peak in combined acceleration, approximately 3.15g, accompanied by a pronounced wave crest in combined angular velocity, around 200 degrees. Additionally, a notable change exceeding 60 degrees occurs in one of the three axis angles of the acceleration sensor[15,21]. Based on these observations, the fall detection algorithm implemented in this study is depicted in Figure6.…”

mentioning

confidence: 95%

“…This device captures vital sign parameters such as heart rate, body temperature, and blood oxygen during physical activity, and relays this information for statistical analysis and timely alerts on abnormalities through IoT communication networks. Nedjai-Merrouche et al [15] developed an outdoor multi-mode system using smartphones, capable of real-time heart rate monitoring and fall detection. This system sets thresholds for heart activity and assesses fall accuracy-achieved at 97.77%-along with GPS positioning and emergency alerts.…”

Section: Introductionmentioning

confidence: 99%

A remote sensing image object detection algorithm based on transfer learning

Dongyang Chen

2024

jes

View full text Add to dashboard Cite

Addressing the limitations of conventional heart rate detectors, which solely monitor heart rate without providing alerts during critical incidents, a multifunctional heart rate detection system has been developed. This system incorporates a GD32 as the main controller and utilizes a pulse sensor for heart rate monitoring, an MLX90614 infrared temperature sensor for body temperature measurements, an MPU6050 acceleration sensor for motion posture detection, and a SIM868 module for GPS positioning and server communication. This comprehensive setup allows for real-time monitoring of the wearer's heart rate and body temperature, assessment of fall incidents based on motion posture data, and regular uploading of health data to a server. Additionally, the system is equipped with a one-touch emergency call feature. In the event of abnormal heart rate readings or a fall, it triggers an alarm, acquires GPS coordinates, and automatically dials an emergency contact number. After thorough testing and verification, the system has demonstrated full functionality with heart rate data accuracy within the expected error range, offering a reliable safety mechanism for patients while on the move.

show abstract

Outdoor multimodal system based on smartphone for health monitoring and incident detection

Cited by 8 publications

References 47 publications

Multimodal System of Ambient Assistance Services for Human Activity Monitoring

Multimodal System of Ambient Assistance Services for Human Activity Monitoring

Development of a mobile health infrastructure for non-communicable diseases using design science research method: a case study

A remote sensing image object detection algorithm based on transfer learning

Contact Info

Product

Resources

About