scholarly journals Innovative Head-Mounted System Based on Inertial Sensors and Magnetometer for Detecting Falling Movements

Sensors ◽  
2020 ◽  
Vol 20 (20) ◽  
pp. 5774
Author(s):  
Chih-Lung Lin ◽  
Wen-Ching Chiu ◽  
Ting-Ching Chu ◽  
Yuan-Hao Ho ◽  
Fu-Hsing Chen ◽  
...  
Keyword(s):  
Activities Of Daily Living ◽  
Daily Living ◽  
Inertial Sensors ◽  
Daily Life ◽  
Detection System ◽  
Fall Detection ◽  
Experimental Results ◽  
Triaxial Accelerometer ◽  
Timely Manner

This work presents a fall detection system that is worn on the head, where the acceleration and posture are stable such that everyday movement can be identified without disturbing the wearer. Falling movements are recognized by comparing the acceleration and orientation of a wearer’s head using prespecified thresholds. The proposed system consists of a triaxial accelerometer, gyroscope, and magnetometer; as such, a Madgwick’s filter is adopted to improve the accuracy of the estimation of orientation. Moreover, with its integrated Wi-Fi module, the proposed system can notify an emergency contact in a timely manner to provide help for the falling person. Based on experimental results concerning falling movements and activities of daily living, the proposed system achieved a sensitivity of 96.67% in fall detection, with a specificity of 98.27%, and, therefore, is suitable for detecting falling movements in daily life.

scholarly journals IR-UWB Sensor Based Fall Detection Method Using CNN Algorithm

Sensors ◽  
2020 ◽  
Vol 20 (20) ◽  
pp. 5948
Author(s):  
Taekjin Han ◽  
Wonho Kang ◽  
Gyunghyun Choi
Keyword(s):  
Activities Of Daily Living ◽  
Daily Living ◽  
Privacy Preservation ◽  
Detection System ◽  
Fall Detection ◽  
The Elderly ◽  
Ultra Wideband ◽  
Detection Accuracy ◽  
Secondary Damage ◽  
Behavior Types

Falls are the leading cause of fatal injuries in the elderly such as fractures, and secondary damage from falls can lead to death. As such, fall detection is a crucial topic. However, due to the trade-off relationship between privacy preservation, user convenience, and fall detection performance, it is generally difficult to develop a fall detection system that simultaneously satisfies all conditions. The main goal of this study is to build a practical fall detection framework that can effectively classify the various behavior types into “Fall” and “Activities of daily living (ADL)” while securing privacy preservation and user convenience. For this purpose, signal data containing the motion information of objects was collected using a non-contact, unobtrusive, and non-restraint impulse-radio ultra wideband (IR-UWB) radar. These data were then applied to a convolutional neural network (CNN) algorithm to create an object behavior type classifier that can classify the behavior types of objects into “Fall” and “ADL.” The data were collected by actually performing various activities of daily living, including falling. The performance of the classifier yielded satisfactory results. By combining an IR-UWB and CNN algorithm, this study demonstrates the feasibility of building a practical fall detection system that exceeds a certain level of detection accuracy while also ensuring privacy preservation and user convenience.

Effective Falls Detection Method Using Two Tri-Axial Accelerometers

2013 ◽  
Vol 647 ◽  
pp. 854-860
Author(s):  
Gye Rok Jeon ◽  
Young Jae Kim ◽  
Ah Young Jeon ◽  
Sang Hoon Lee ◽  
Jae Hyung Kim ◽  
...  
Keyword(s):  
Activities Of Daily Living ◽  
Daily Living ◽  
Detection Rate ◽  
Elderly Population ◽  
Detection Method ◽  
Detection System ◽  
Fall Detection ◽  
The Elderly ◽  
Detection Systems

Falls detection systems have been developed in recent years because falls are detrimental events that can have a devastating effect on health of the elderly population. Current fall detecting methods mainly employ accelerometer to discriminate falls from activities of daily living (ADL). However, this makes it difficult to distinguish real falls from certain fall-like activities such as jogging and jumping. In this paper, an accurate fall detection system was implemented using two tri-axial accelerometers. By attaching the accelerometers on the chest and the abdomen, our system can effectively differentiate between falls and non-fall events.The Diff_Z and Sum_diff_Z parameter resulted in falls detection rate of 100%, respectively.

Oxygen Desaturation in Daily Life and During a Laboratory-Based Protocol of Activities of Daily Living in COPD: Is There Relationship?

Lung ◽  
2017 ◽  
Vol 196 (1) ◽  
pp. 19-26 ◽  
Author(s):  
Thaís Sant’Anna ◽  
Leila Donária ◽  
Nidia A. Hernandes ◽  
Karina C. Furlanetto ◽  
Décio S. Barbosa ◽  
...  
Keyword(s):  
Activities Of Daily Living ◽  
Daily Living ◽  
Daily Life ◽  
Oxygen Desaturation

scholarly journals The real-time elderly fall posture identifying scheme with wearable sensors

2019 ◽  
Vol 15 (11) ◽  
pp. 155014771988561
Author(s):  
Tao Xu ◽  
Wei Sun ◽  
Shaowei Lu ◽  
Ke-ming Ma ◽  
Xiaoqiang Wang
Keyword(s):  
Activities Of Daily Living ◽  
Real Time ◽  
Daily Living ◽  
Wearable Sensors ◽  
Smart Phone ◽  
The Elderly ◽  
The Body ◽  
Short Message ◽  
Triaxial Accelerometer ◽  
Time Fall

The accidental fall is the major risk for elderly especially under unsupervised states. It is necessary to real-time monitor fall postures for elderly. This paper proposes the fall posture identifying scheme with wearable sensors including MPU6050 and flexible graphene/rubber. MPU6050 is located at the waist to monitor the attitude of the body with triaxial accelerometer and gyroscope. The graphene/rubber sensors are located at the knees to monitor the moving actions of the legs. A real-time fall postures identifying algorithm is proposed by the integration of triaxial accelerometer, tilt angles, and the bending angles from the graphene/rubber sensors. A volunteer is engaged to emulate elderly physical behaviors in performing four activities of daily living and six fall postures. Four basic fall down postures can be identified with MPU6050. Integrated with graphene/rubber sensors, two more fall postures are correctly identified by the proposed scheme. Test results show that the accuracy for activities of daily living detection is 93.5% and that for fall posture identifying is 90%. After the fall postures are identified, the proposed system transmits the fall posture to the smart phone carried by the elderly via Bluetooth. Finally, the posture and location are transmitted to the specified mobile phone by short message.

scholarly journals Influence of socioeconomic factors on daily life activities and quality of life of Thai elderly

2017 ◽  
Vol 6 (1) ◽  
Author(s):  
Ratana Somrongthong ◽  
Sunanta Wongchalee ◽  
Chandrika Ramakrishnan ◽  
Donnapa Hongthong ◽  
Korravarn Yodmai ◽  
...  
Keyword(s):  
Quality Of Life ◽  
Activities Of Daily Living ◽  
Socioeconomic Factors ◽  
Functional Capacity ◽  
Daily Living ◽  
Daily Life ◽  
Household Survey ◽  
Cluster Sampling ◽  
Daily Life Activities

<em>Background</em>: The increasing number of older people is a significant issue in Thailand, resulted in growing demands of health and social welfare services. The study aim was to explore the influence of socioeconomic factors on activities of daily living and quality of life of Thai seniors. <br /><em>Design and methods:</em> Using randomised cluster sampling, one province was sampled from each of the Central, North, Northeast and South regions, then one subdistrict sampled in each province, and a household survey used to identify the sample of 1678 seniors aged 60 years and over. The Mann-Whitney U-test and binary logistic regression were used to compare and determine the association of socioeconomic variables on quality of life and activities of daily living. <br /><em>Results</em>: The findings showed that sociodemographic and socioeconomic factors were significantly related to functional capacity of daily living. Education levels were strongly associated with daily life activities, with 3.55 adjusted ORs for respondents with secondary school education. Gender was important, with females comprising 61% of dependent respondents but only 47% of independent respondents. Seniors with low incomes were more likely to be anxious in the past, present and future and less likely to accept death in the late stage, with 1.40 Adjusted ORs (95%CI: 1.02-1.92), and 0.72 (95%CI: 0.53-0.98), respectively. However, they were more likely to engage in social activities. <br /><em>Conclusions</em>: While socioeconomic factors strongly indicated the functional capacity to live independently, a good quality of life also required other factors leading to happiness and life satisfaction.

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