A comparison between heart-rate monitoring smart devices for ambient assisted living

2021 ◽  
Author(s):  
Majid H. Alsulami ◽  
Saleh N. Almuayqil ◽  
Anthony S. Atkins
Keyword(s):  
Heart Rate ◽  
Assisted Living ◽  
Ambient Assisted Living ◽  
Smart Devices ◽  
Heart Rate Monitoring

Ambient Assisted Living Systems: The Scope of Research and Development

2017 ◽  
Vol 3 (2) ◽  
pp. 21
Author(s):  
Ashish D Patel ◽  
Jigarkumar H. Shah
Keyword(s):  
Assisted Living ◽  
Independent Living ◽  
Intelligent Systems ◽  
New Technologies ◽  
Ambient Assisted Living ◽  
Smart Devices ◽  
Economic Changes ◽  
Aged Population ◽  
The People ◽  
Living Person

The aged population of the world is increasing by a large factor due to the availability of medical and other facilities. As the number grows rapidly, requirements of this segment of age (65+) are increasing rapidly as well as the percentage of aged persons living alone is also increasing with the same rate due to the inevitable socio-economic changes. This situation demands the solution of many problems like loneliness, chronic conditions, social interaction, transportation, day-to-day life and many more for independent living person. A large part of aged population may not be able to interact directly with new technologies. This sought some serious development towards the use of intelligent systems i.e. smart devices which helps the people with their inability to use the available as well future solutions. Ambient Assisted Living (AAL) is the answer to these problems. In this paper, issues related to AAL systems are studied. Study of challenges and limitations of this comparatively new field will help the designers to remove the barriers of AAL systems.

scholarly journals Efficient methods for acute stress detection using heart rate variability data from Ambient Assisted Living sensors

2021 ◽  
Vol 20 (1) ◽  
Author(s):  
Benedek Szakonyi ◽  
István Vassányi ◽  
Edit Schumacher ◽  
István Kósa
Keyword(s):  
Heart Rate ◽  
Heart Rate Variability ◽  
Time Domain ◽  
Assisted Living ◽  
Acute Stress ◽  
Ambient Assisted Living ◽  
Portable Devices ◽  
Stress Detection ◽  
Non Linear ◽  
Variability Data

Abstract Background Using Ambient Assisted Living sensors to detect acute stress could help people mitigate the harmful effects of everyday stressful situations. This would help both the healthy and those affected more by sudden stressors, e.g., people with diabetes or heart conditions. The study aimed to develop a method for providing reliable stress detection based on heart rate variability features extracted from portable devices. Methods Features extracted from portable electrocardiogram sensor recordings were used for training various classification algorithms for stress detection purposes. Data were recorded in a clinical trial with 7 participants and two stressors, the Trier Social Stress Test and the Stroop colour word test, both validated by standardised questionnaires. Different heart rate variability feature sets (all, time-domain and non-linear only, frequency-domain only) were tested to investigate how classification performance is affected, in addition to various time window length setups and participant-wise training sessions. The accuracy and F1 score of the trained models were compared and analysed. Results The best results were achieved with models using time-domain and non-linear heart rate variability features with 5-min-long overlapping time windows, yielding 96.31% accuracy and 96.26% F1 score. Shorter overlapping windows had slightly lower performance, with 91.62–94.55% accuracy and 91.77–94.55% F1 score ranges. Non-overlapping window configurations were less effective, with both accuracy and F1 score below 88%. For participant-wise learning, average F1 scores of 99.47%, 98.93% and 96.1% were achieved for feature sets using all, time-domain and non-linear, and frequency-domain features, respectively. Conclusion The tested stress detector models based on heart rate variability data recorded by a single electrocardiogram sensor performed just as well as those published in the literature working with multiple sensors, or even better. This suggests that once portable devices such as smartwatches provide reliable hear rate variability recordings, efficient stress detection can be achieved without the need for additional physiological measurements.

scholarly journals Social-aware Event Handling within the FallRisk Project

2017 ◽  
Vol 56 (01) ◽  
pp. 63-73 ◽  
Author(s):  
Jan Van den Bergh ◽  
Sven Coppers ◽  
Shirley Elprama ◽  
Jelle Nelis ◽  
Stijn Verstichel ◽  
...  
Keyword(s):  
Assisted Living ◽  
Ambient Assisted Living ◽  
Fall Detection ◽  
Smart Devices ◽  
Accurate Analysis ◽  
Fall Incident ◽  
Detection Systems ◽  
Event Handling ◽  
Current Guidelines ◽  
The Internet Of Things

SummaryObjectives: With the uprise of the Internet of Things, wearables and smartphones are moving to the foreground. Ambient Assisted Living solutions are, for example, created to facilitate ageing in place. One example of such systems are fall detection systems. Currently, there exists a wide variety of fall detection systems using different methodologies and technologies. However, these systems often do not take into account the fall handling process, which starts after a fall is identified or this process only consists of sending a notification. The FallRisk system delivers an accurate analysis of incidents occurring in the home of the older adults using several sensors and smart devices. Moreover, the input from these devices can be used to create a social-aware event handling process, which leads to assisting the older adult as soon as possible and in the best possible way.Methods: The FallRisk system consists of several components, located in different places. When an incident is identified by the FallRisk system, the event handling process will be followed to assess the fall incident and select the most appropriate caregiver, based on the input of the smartphones of the caregivers. In this process, availability and location are automatically taken into account.Results: The event handling process was evaluated during a decision tree workshop to verify if the current day practices reflect the requirements of all the stakeholders. Other knowledge, which is uncovered during this workshop can be taken into account to further improve the process.Conclusions: The FallRisk offers a way to detect fall incidents in an accurate way and uses context information to assign the incident to the most appropriate caregiver. This way, the consequences of the fall are minimized and help is at location as fast as possible. It could be concluded that the current guidelines on fall handling reflect the needs of the stakeholders. However, current technology evolutions, such as the uptake of wearables and smartphones, enables the improvement of these guidelines, such as the automatic ordering of the caregivers based on their location and availability.

scholarly journals Stress Detection Using Low Cost Heart Rate Sensors

2016 ◽  
Vol 2016 ◽  
pp. 1-13 ◽  
Author(s):  
Mario Salai ◽  
István Vassányi ◽  
István Kósa
Keyword(s):  
Heart Rate ◽  
Time Domain ◽  
Assisted Living ◽  
Low Cost ◽  
Ambient Assisted Living ◽  
Average Error ◽  
Stress Detection ◽  
Simple Method ◽  
Device Validation ◽  
Rate Sensor

The automated detection of stress is a central problem for ambient assisted living solutions. The paper presents the concepts and results of two studies targeted at stress detection with a low cost heart rate sensor, a chest belt. In the device validation study (n=5), we compared heart rate data and other features from the belt to those measured by a gold standard device to assess the reliability of the sensor. With simple synchronization and data cleaning algorithm, we were able to select highly (>97%) correlated, low average error (2.2%) data segments of considerable length from the chest data for further processing. The protocol for the clinical study (n=46) included a relax phase followed by a phase with provoked mental stress, 10 minutes each. We developed a simple method for the detection of the stress using only three time-domain features of the heart rate signal. The method produced accuracy of 74.6%, sensitivity of 75.0%, and specificity of 74.2%, which is impressive compared to the performance of two state-of-the-art methods run on the same data. Since the proposed method uses only time-domain features, it can be efficiently implemented on mobile devices.

scholarly journals Heart Rate Monitoring of Anaesthesiology Residents during the Airway Management of COVID 19 Suspect Patients: An Observational Study

2021 ◽  
Author(s):  
Bhavna Gupta ◽  
Vijay Adabala ◽  
Pratik Tuppad ◽  
Unni Kannan
Keyword(s):  
Heart Rate ◽  
Tertiary Care ◽  
Perioperative Period ◽  
Smart Devices ◽  
P Value ◽  
Heart Rate Monitoring ◽  
Indirect Measure ◽  
Percentage Points ◽  
Observational Cohort ◽  
The Difference

Background: Anaesthesiologists undergo shear stress during the perioperative period, which was further increased during the COVID 19 pandemic. Many observational studies were done to find out the stress levels of the residents. Methods: This was a prospective observational cohort study of Anaesthesiology residents in a tertiary care academic institution. We have measured the minute to minute heart rate variability which can be an indirect measure of stress level with the help of wrist band MI 4 which works on the principle of PPG. Results: The difference between baseline HR and resting HR was observed to be substantial (p value 0.115 and 0.000 respectively). The percentage rise in heart rate during intubation from resting heart rate was 42.79 ± 25.54 percentage points. Conclusion: Users can use this type of ongoing information as a feedback option to increase their work efficacy. Understanding how to use these smart devices will assist us in balancing our stress-free day-to-day activities.

scholarly journals Multimodal Sensor Data Integration for Indoor Positioning in Ambient-Assisted Living Environments

2020 ◽  
Vol 2020 ◽  
pp. 1-16
Author(s):  
Emilio Sansano-Sansano ◽  
Óscar Belmonte-Fernández ◽  
Raúl Montoliu ◽  
Arturo Gascó-Compte ◽  
Antonio Caballer-Miedes
Keyword(s):  
Assisted Living ◽  
Ambient Assisted Living ◽  
Indoor Positioning ◽  
Training Data ◽  
Sensor Data ◽  
Smart Devices ◽  
Positioning System ◽  
Positioning Systems ◽  
Future Performance ◽  
Data Scaling

A reliable Indoor Positioning System (IPS) is a crucial part of the Ambient-Assisted Living (AAL) concept. The use of Wi-Fi fingerprinting techniques to determine the location of the user, based on the Received Signal Strength Indication (RSSI) mapping, avoids the need to deploy a dedicated positioning infrastructure but comes with its own issues. Heterogeneity of devices and RSSI variability in space and time due to environment changing conditions pose a challenge to positioning systems based on this technique. The primary purpose of this research is to examine the viability of leveraging other sensors in aiding the positioning system to provide more accurate predictions. In particular, the experiments presented in this work show that Inertial Motion Units (IMU), which are present by default in smart devices such as smartphones or smartwatches, can increase the performance of Indoor Positioning Systems in AAL environments. Furthermore, this paper assesses a set of techniques to predict the future performance of the positioning system based on the training data, as well as complementary strategies such as data scaling and the use of consecutive Wi-Fi scanning to further improve the reliability of the IPS predictions. This research shows that a robust positioning estimation can be derived from such strategies.

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