Weakly Supervised Recognition of Daily Life Activities with Wearable Sensors

2011 ◽  
Vol 33 (12) ◽  
pp. 2521-2537 ◽  
Author(s):  
M. Stikic ◽  
D. Larlus ◽  
S. Ebert ◽  
B. Schiele
Keyword(s):  
Daily Life ◽  
Wearable Sensors ◽  
Daily Life Activities ◽  
Weakly Supervised

scholarly journals Continuous Monitoring of Vital Signs With Wearable Sensors During Daily Life Activities: Validation Study

10.2196/30863 ◽  
2022 ◽  
Vol 6 (1) ◽  
pp. e30863
Author(s):  
Marjolein E Haveman ◽  
Mathilde C van Rossum ◽  
Roswita M E Vaseur ◽  
Claire van der Riet ◽  
Richte C L Schuurmann ◽  
...  
Keyword(s):  
Physical Activity ◽  
Missing Data ◽  
Concurrent Validity ◽  
Daily Life ◽  
Wearable Sensors ◽  
Vital Signs ◽  
Living Environment ◽  
Mean Difference ◽  
Data Availability ◽  
Daily Life Activities

Background Continuous telemonitoring of vital signs in a clinical or home setting may lead to improved knowledge of patients’ baseline vital signs and earlier detection of patient deterioration, and it may also facilitate the migration of care toward home. Little is known about the performance of available wearable sensors, especially during daily life activities, although accurate technology is critical for clinical decision-making. Objective The aim of this study is to assess the data availability, accuracy, and concurrent validity of vital sign data measured with wearable sensors in volunteers during various daily life activities in a simulated free-living environment. Methods Volunteers were equipped with 4 wearable sensors (Everion placed on the left and right arms, VitalPatch, and Fitbit Charge 3) and 2 reference devices (Oxycon Mobile and iButton) to obtain continuous measurements of heart rate (HR), respiratory rate (RR), oxygen saturation (SpO2), and temperature. Participants performed standardized activities, including resting, walking, metronome breathing, chores, stationary cycling, and recovery afterward. Data availability was measured as the percentage of missing data. Accuracy was evaluated by the median absolute percentage error (MAPE) and concurrent validity using the Bland-Altman plot with mean difference and 95% limits of agreement (LoA). Results A total of 20 volunteers (median age 64 years, range 20-74 years) were included. Data availability was high for all vital signs measured by VitalPatch and for HR and temperature measured by Everion. Data availability for HR was the lowest for Fitbit (4807/13,680, 35.14% missing data points). For SpO2 measured by Everion, median percentages of missing data of up to 100% were noted. The overall accuracy of HR was high for all wearable sensors, except during walking. For RR, an overall MAPE of 8.6% was noted for VitalPatch and that of 18.9% for Everion, with a higher MAPE noted during physical activity (up to 27.1%) for both sensors. The accuracy of temperature was high for VitalPatch (MAPE up to 1.7%), and it decreased for Everion (MAPE from 6.3% to 9%). Bland-Altman analyses showed small mean differences of VitalPatch for HR (0.1 beats/min [bpm]), RR (−0.1 breaths/min), and temperature (0.5 °C). Everion and Fitbit underestimated HR up to 5.3 (LoA of −39.0 to 28.3) bpm and 11.4 (LoA of −53.8 to 30.9) bpm, respectively. Everion had a small mean difference with large LoA (−10.8 to 10.4 breaths/min) for RR, underestimated SpO2 (>1%), and overestimated temperature up to 2.9 °C. Conclusions Data availability, accuracy, and concurrent validity of the studied wearable sensors varied and differed according to activity. In this study, the accuracy of all sensors decreased with physical activity. Of the tested sensors, VitalPatch was found to be the most accurate and valid for vital signs monitoring.

scholarly journals Continuous Monitoring of Vital Signs With Wearable Sensors During Daily Life Activities: Validation Study (Preprint)

2021 ◽  
Author(s):  
Marjolein E Haveman ◽  
Mathilde C van Rossum ◽  
Roswita M E Vaseur ◽  
Claire van der Riet ◽  
Richte C L Schuurmann ◽  
...  
Keyword(s):  
Physical Activity ◽  
Missing Data ◽  
Concurrent Validity ◽  
Daily Life ◽  
Wearable Sensors ◽  
Vital Signs ◽  
Living Environment ◽  
Mean Difference ◽  
Data Availability ◽  
Daily Life Activities

BACKGROUND Continuous telemonitoring of vital signs in a clinical or home setting may lead to improved knowledge of patients’ baseline vital signs and earlier detection of patient deterioration, and it may also facilitate the migration of care toward home. Little is known about the performance of available wearable sensors, especially during daily life activities, although accurate technology is critical for clinical decision-making. OBJECTIVE The aim of this study is to assess the data availability, accuracy, and concurrent validity of vital sign data measured with wearable sensors in volunteers during various daily life activities in a simulated free-living environment. METHODS Volunteers were equipped with 4 wearable sensors (Everion placed on the left and right arms, VitalPatch, and Fitbit Charge 3) and 2 reference devices (Oxycon Mobile and iButton) to obtain continuous measurements of heart rate (HR), respiratory rate (RR), oxygen saturation (SpO<sub>2</sub>), and temperature. Participants performed standardized activities, including resting, walking, metronome breathing, chores, stationary cycling, and recovery afterward. Data availability was measured as the percentage of missing data. Accuracy was evaluated by the median absolute percentage error (MAPE) and concurrent validity using the Bland-Altman plot with mean difference and 95% limits of agreement (LoA). RESULTS A total of 20 volunteers (median age 64 years, range 20-74 years) were included. Data availability was high for all vital signs measured by VitalPatch and for HR and temperature measured by Everion. Data availability for HR was the lowest for Fitbit (4807/13,680, 35.14% missing data points). For SpO<sub>2</sub> measured by Everion, median percentages of missing data of up to 100% were noted. The overall accuracy of HR was high for all wearable sensors, except during walking. For RR, an overall MAPE of 8.6% was noted for VitalPatch and that of 18.9% for Everion, with a higher MAPE noted during physical activity (up to 27.1%) for both sensors. The accuracy of temperature was high for VitalPatch (MAPE up to 1.7%), and it decreased for Everion (MAPE from 6.3% to 9%). Bland-Altman analyses showed small mean differences of VitalPatch for HR (0.1 beats/min [bpm]), RR (−0.1 breaths/min), and temperature (0.5 °C). Everion and Fitbit underestimated HR up to 5.3 (LoA of −39.0 to 28.3) bpm and 11.4 (LoA of −53.8 to 30.9) bpm, respectively. Everion had a small mean difference with large LoA (−10.8 to 10.4 breaths/min) for RR, underestimated SpO<sub>2</sub> (&gt;1%), and overestimated temperature up to 2.9 °C. CONCLUSIONS Data availability, accuracy, and concurrent validity of the studied wearable sensors varied and differed according to activity. In this study, the accuracy of all sensors decreased with physical activity. Of the tested sensors, VitalPatch was found to be the most accurate and valid for vital signs monitoring. CLINICALTRIAL

Mobile phone apps’ support for people with mild dementia: A systematic review (Preprint)

2019 ◽  
Author(s):  
Leona Cilar ◽  
Lucija Gosak ◽  
Amanda Briggs ◽  
Klavdija Čuček Trifkovič ◽  
Tracy McClelland ◽  
...  
Keyword(s):  
Mobile Phone ◽  
Thematic Analysis ◽  
Daily Life ◽  
General Term ◽  
Cognitive Domain ◽  
Mild Dementia ◽  
Daily Life Activities ◽  
Human Needs ◽  
People With Dementia ◽  
System Usability Scale

BACKGROUND Dementia is a general term for various disorders characterized by memory impairment and loss of at least one cognitive domain. People with dementia are faced with different difficulties in their daily life activities (DLA). With the use of modern technologies, such as mobile phone apps – often called health apps, their difficulties can be alleviated. OBJECTIVE The aim of this paper was to systematically search, analyze and synthetize mobile phone apps designed to support people with mild dementia in daily life activities in two apps bases: Apple App Store and Google Play Store. METHODS A search was conducted in May 2019 following PRISMA recommendations. Results were analyzed and displayed as tables and graphs. Results were synthetized using thematic analysis which was conducted from 14 components, based on human needs for categorized nursing activities. Mobile phone apps were assessed for quality using the System Usability Scale. RESULTS A total of 15 mobile phone apps were identified applying inclusion and exclusion criteria. Five major themes were identified with thematic analysis: multi-component DLA, communication and feelings, recreation, eating and drinking, and movement. Most of the apps (73%) of the apps were not mentioned in scientific literature. CONCLUSIONS There are many mobile phone apps available in mobile phone markets for the support for people with mild dementia; yet only a few of them are focused on challenges in daily life activities. Most of the available apps were not evaluated nor assessed for quality.

Algorithmic Analysis of Automatic Attendance System using Facial Recognition

2022 ◽  
Vol 14 (1) ◽  
pp. 0-0
Keyword(s):  
Face Recognition ◽  
Daily Life ◽  
Real Life ◽  
Student Attendance ◽  
Surveillance Camera ◽  
Daily Life Activities ◽  
The Face ◽  
Algorithmic Analysis ◽  
Human Faces ◽  
Face Biometrics

Attendance management can become a tedious task for teachers if it is performed manually.. This problem can be solved with the help of an automatic attendance management system. But validation is one of the main issues in the system. Generally, biometrics are used in the smart automatic attendance system. Managing attendance with the help of face recognition is one of the biometric methods with better efficiency as compared to others. Smart Attendance with the help of instant face recognition is a real-life solution that helps in handling daily life activities and maintaining a student attendance system. Face recognition-based attendance system uses face biometrics which is based on high resolution monitor video and other technologies to recognize the face of the student. In project, the system will be able to find and recognize human faces fast and accurately with the help of images or videos that will be captured through a surveillance camera. It will convert the frames of the video into images so that our system can easily search that image in the attendance database.

Mental time travel and functional daily life activities in neglect patients: Recovery effects of rehabilitation by prism adaptation

Cortex ◽  
2019 ◽  
Vol 113 ◽  
pp. 141-155 ◽  
Author(s):  
Filomena Anelli ◽  
Stefano Avanzi ◽  
Alessio Damora ◽  
Mauro Mancuso ◽  
Francesca Frassinetti
Keyword(s):  
Daily Life ◽  
Time Travel ◽  
Prism Adaptation ◽  
Mental Time Travel ◽  
Daily Life Activities

scholarly journals Participation in daily life activities and its relationship to strength and functional measures in boys with Duchenne muscular dystrophy

2014 ◽  
Vol 36 (22) ◽  
pp. 1918-1923 ◽  
Author(s):  
Roxanna M. Bendixen ◽  
Donovan J. Lott ◽  
Claudia Senesac ◽  
Sunita Mathur ◽  
Krista Vandenborne
Keyword(s):  
Duchenne Muscular Dystrophy ◽  
Muscular Dystrophy ◽  
Daily Life ◽  
Daily Life Activities ◽  
Functional Measures
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