scholarly journals Elderly Care Based on Hand Gestures Using Kinect Sensor

Computers ◽  
2020 ◽  
Vol 10 (1) ◽  
pp. 5
Author(s):  
Munir Oudah ◽  
Ali Al-Naji ◽  
Javaan Chahl
Keyword(s):  
Elderly People ◽  
Elderly Care ◽  
Research Field ◽  
Microsoft Kinect ◽  
Elderly Subjects ◽  
Depth Sensor ◽  
Hand Gestures ◽  
Natural Interaction ◽  
Kinect V2 ◽  
Joint Tracking

Technological advances have allowed hand gestures to become an important research field especially in applications such as health care and assisting applications for elderly people, providing a natural interaction with the assisting system through a camera by making specific gestures. In this study, we proposed three different scenarios using a Microsoft Kinect V2 depth sensor then evaluated the effectiveness of the outcomes. The first scenario used joint tracking combined with a depth threshold to enhance hand segmentation and efficiently recognise the number of fingers extended. The second scenario utilised the metadata parameters provided by the Kinect V2 depth sensor, which provided 11 parameters related to the tracked body and gave information about three gestures for each hand. The third scenario used a simple convolutional neural network with joint tracking by depth metadata to recognise and classify five hand gesture categories. In this study, deaf-mute elderly people performed five different hand gestures, each related to a specific request, such as needing water, meal, toilet, help and medicine. Next, the request was sent via the global system for mobile communication (GSM) as a text message to the care provider’s smartphone because the elderly subjects could not execute any activity independently.

scholarly journals Computer Vision for Elderly Care Based on Hand Gestures

2020 ◽  
Author(s):  
Munir Oudah ◽  
Ali Al-Naji ◽  
Javaan Chahl
Keyword(s):  
Elderly People ◽  
Elderly Care ◽  
Microsoft Kinect ◽  
Depth Sensor ◽  
Hand Gestures ◽  
Natural Interaction ◽  
Kinect V2 ◽  
Hand Segmentation ◽  
Care Of Elderly ◽  
Joint Tracking

Hand gestures may play an important role in medical applications for health care of elderly people, where providing a natural interaction for different requests can be executed by making specific gestures. In this study we explored three different scenarios using a Microsoft Kinect V2 depth sensor then evaluated the effectiveness of the outcomes. The first scenario utilized the default system embedded in the Kinect V2 sensor, which depth metadata gives 11 parameters related to the tracked body with five gestures for each hand. The second scenario used joint tracking provided by Kinect depth metadata and depth threshold together to enhance hand segmentation and efficiently recognize the number of fingers extended. The third scenario used a simple convolutional neural network with joint tracking by depth metadata to recognize five categories of gestures. In this study, deaf-mute elderly people execute five different hand gestures to indicate a specific request, such as needing water, meal, toilet, help and medicine. Then, the requests were sent to the care provider’s smartphone because elderly people could not execute any activity independently. The system transferred these requests as a message through the global system for mobile communication (GSM) using a microcontroller.

scholarly journals AIR-Act2Act: Human–human interaction dataset for teaching non-verbal social behaviors to robots

2021 ◽  
pp. 027836492199067
Author(s):  
Woo-Ri Ko ◽  
Minsu Jang ◽  
Jaeyeon Lee ◽  
Jaehong Kim
Keyword(s):  
Social Skills ◽  
Elderly People ◽  
Elderly Care ◽  
Social Behaviors ◽  
The Elderly ◽  
Human Interaction ◽  
Microsoft Kinect ◽  
Service Robots ◽  
Interaction Dataset ◽  
Humanoid Nao

To better interact with users, a social robot should understand the users’ behavior, infer the intention, and respond appropriately. Machine learning is one way of implementing robot intelligence. It provides the ability to automatically learn and improve from experience instead of explicitly telling the robot what to do. Social skills can also be learned through watching human–human interaction videos. However, human–human interaction datasets are relatively scarce to learn interactions that occur in various situations. Moreover, we aim to use service robots in the elderly care domain; however, there has been no interaction dataset collected for this domain. For this reason, we introduce a human–human interaction dataset for teaching non-verbal social behaviors to robots. It is the only interaction dataset that elderly people have participated in as performers. We recruited 100 elderly people and 2 college students to perform 10 interactions in an indoor environment. The entire dataset has 5,000 interaction samples, each of which contains depth maps, body indexes, and 3D skeletal data that are captured with three Microsoft Kinect v2 sensors. In addition, we provide the joint angles of a humanoid NAO robot which are converted from the human behavior that robots need to learn. The dataset and useful Python scripts are available for download at https://github.com/ai4r/AIR-Act2Act . It can be used to not only teach social skills to robots but also benchmark action recognition algorithms.

scholarly journals Hand Gestures for Elderly Care Using a Microsoft Kinect

2020 ◽  
Vol 12 (3) ◽  
Author(s):  
Munir Oudah ◽  
Ali Al-Naji ◽  
Javaan Chahl
Keyword(s):  
Elderly Care ◽  
Microsoft Kinect ◽  
Hand Gestures

scholarly journals Anthropometric characteristics of healthy Italian nonagenarians and centenarians

1997 ◽  
Vol 77 (1) ◽  
pp. 9-17 ◽  
Author(s):  
Giovanni Ravaglia ◽  
Pietro Morini ◽  
Paola Forti ◽  
Fabiola Maioli ◽  
Federica Boschi ◽  
...  
Keyword(s):  
Elderly People ◽  
Fat Mass ◽  
Nutritional Assessment ◽  
Age Groups ◽  
Elderly Subjects ◽  
Old People ◽  
Anthropometric Indices ◽  
Anthropometric Characteristics ◽  
Successive Generations ◽  
British People

Available anthropometric reference values for elderly people do not include specific norms for over-90-year-old subjects despite their increasing number. In the present study, weight, height and a number of anthropometric variables related to body muscle and fat mass were collected from fifty-seven nonagenarian and forty-one centenarian healthy, non-institutionalized subjects living in an Italian area. Recumbent anthropometry was used to avoid errors associated with impaired mobility. Nonagenarians and centenarian men were taller and heavier than women of corresponding age and had a greater amount of muscle and trunk fat, whereas women showed a marked peripheral adipose distribution. Anthropometric values of both age-groups were generally lower than published norms for 70–89-year-old American and European elderly people. However, differences were less marked when comparing Italian nonagenarians and centenarians with French and British people aged 85 years and over than when comparing Italian subjects with American octogenarians and younger European elderly people. Taken together these findings suggest a dramatic loss of muscle and fat mass in over-90-year-old subjects with respect to younger elderly people. However, changes between successive generations and geographical influences cannot be excluded. The need for local and age-specific norms in nutritional assessment of over-90-year-old people is emphasized. It is also suggested that current anthropometric indices may not be reliable when evaluating the oldest elderly subjects.

Cold-induced Increases in Erythrocyte Count, Plasma Cholesterol and Plasma Fibrinogen of Elderly People without a Comparable Rise in Protein C or Factor X

1994 ◽  
Vol 86 (1) ◽  
pp. 43-48 ◽  
Author(s):  
Penelope J. Neild ◽  
Denise Syndercombe-Court ◽  
W. R. Keatinge ◽  
G. C. Donaldson ◽  
M. Mattock ◽  
...  
Keyword(s):  
Elderly People ◽  
Plasma Volume ◽  
Arterial Thrombosis ◽  
Protein C ◽  
Plasma Cholesterol ◽  
The Elderly ◽  
Erythrocyte Count ◽  
Elderly Subjects ◽  
Factor X ◽  
Young Subjects

1. Six elderly (66-71 years) and six young (20-23 years) subjects (half of each group women) were cooled for 2 h in moving air at 18°C to investigate possible causes of increased mortality from arterial thrombosis among elderly people in cold weather. Compared with thermoneutral control experiments, skin temperature (trunk) fell from 35.5 to 29.5°C, with little change in core temperature. 2. Erythrocyte count rose in the cold from 4.29 to 4.69 × 1012/l, without a change in mean corpuscular volume, indicating a 14% or 438 ml decline in plasma volume; increased excretion of water, Na+ and K+ accounted for loss of only 179 ml of extracellular water. 3. Plasma cholesterol and fibrinogen concentrations rose in the elderly subjects from 4.9 mmol/l and 2.97 g/l (control) to 5.45 mmol/l and 3.39 g/l in the cold, and in the young subjects from 3.33 mmol/l and 1.84 g/l (control) to 3.77 mmol/l and 2.07 g/l in the cold. Increases were significant for the elderly subjects, the young subjects and the group as a whole, except for cholesterol in the young subjects, and all were close to those expected from the fall in plasma volume. 4. Plasma levels of Protein C and factor X did not increase significantly in the cold in the elderly subjects, young subjects, or the group as a whole. 5. The results suggest that loss of plasma fluid in the cold concentrates major risk factors for arterial thrombosis, while small molecules, including protective Protein C, redistribute to interstitial fluid.

scholarly journals Evaluating the effectiveness of dichotic training in the elderly adults: a single subject study

2021 ◽  
Author(s):  
Zahra Shahidipour ◽  
Saeid Farahani ◽  
Ghassem Mohammadkhani ◽  
Elham Tavanai ◽  
Nariman Rahbar ◽  
...  
Keyword(s):  
Training Program ◽  
Elderly People ◽  
Dichotic Listening ◽  
Poor Performance ◽  
The Elderly ◽  
Elderly Subjects ◽  
Intensity Difference ◽  
Single Subject ◽  
Elderly Adults ◽  
Dichotic Training

Background and Aim: Elderly people usually show poor performance in dichotic listening tasks. In this condition, the left ear being often the weaker one shows a performance below the normal limits. Studies have shown the effectiveness of dichotic listening training in auditory and language processing for adults and children with neurological disorders. This study aimed to develop a home-version of dichotic training and investigate its effectiveness in elderly adults. Methods: Participants in this single-subject interventional study (AB design) were four elderly subjects (two males and two females) aged 65−75 years. The main inclusion criteria were dichotic listening deficit demonstrated by the dichotic digit test (DDT), no neurological or cognitive disorders, and normal hearing threshold. Dichotic listening training was performed with an informal home-version of dichotic interaural intensity difference (DIID) training program for seven weeks. DDT was performed seven consecutive weeks before (phase A) and after the intervention (phase B) at the end of each week. Results: Data were analyzed by single-subject study statistics. Findings demonstrated an improvement in DDT scores for the left ear and decrease in right ear advantage scores in all the elderly adults after DIID training program. It seems that this training program could remediate poor performance in dichotic listening tasks in elderly people. Conclusion: The advantage of this method is that it can be easily done at home and is costeffective. However, further studies are needed to approve the neuroplasticity and structural changes in the brain after the DIID training program in this population. Keywords: Auditory rehabilitation; dichotic training; dichotic listening; elderly; singlesubject study

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