Development of a human fall detection system based on depth maps

Assistive care related products are increasingly in demand with the recent developments in health sector associated technologies. There are several studies concerned in improving and eliminating barriers in providing quality health care services to all people, especially elderly who live alone and those who cannot move from their home for various reasons such as disable, overweight. Among them, human fall detection systems play an important role in our daily life, because fall is the main obstacle for elderly people to live independently and it is also a major health concern due to aging population. The three basic approaches used to develop human fall detection systems include some sort of wearable devices, ambient based devices or non-invasive vision based devices using live cameras. Most of such systems are either based on wearable or ambient sensor which is very often rejected by users due to the high false alarm and difficulties in carrying them during their daily life activities. Thus, this study proposes a non-invasive human fall detection system based on the height, velocity, statistical analysis, fall risk factors and position of the subject using depth information from Microsoft Kinect sensor. Classification of human fall from other activities of daily life is accomplished using height and velocity of the subject extracted from the depth information after considering the fall risk level of the user. Acceleration and activity detection are also employed if velocity and height fail to classify the activity. Finally position of the subject is identified for fall confirmation or statistical analysis is conducted to verify the fall event. From the experimental results, the proposed system was able to achieve an average accuracy of 98.3% with sensitivity of 100% and specificity of 97.7%. The proposed system accurately distinguished all the fall events from other activities of daily life

Development of a user-adaptable human fall detection based on fall risk levels using depth sensor

Unintentional falls are a major public health concern for many communities, especially with aging populations. There are various approaches used to classify human activities for fall detection. Related studies have employed wearable, non-invasive sensors, video cameras and depth sensor-based approaches to develop such monitoring systems. The proposed approach in this study uses a depth sensor and employs a unique procedure which identifies the fall risk levels to adapt the algorithm for different people with their physical strength to withstand falls. The inclusion of the fall risk level identification, further enhanced and improved the accuracy of the fall detection. The experimental results showed promising performance in adapting the algorithm for people with different fall risk levels for fall detection

A Study on Human Fall Detection Systems: Daily Activity Classification and Sensing Techniques

Fall detection for elderly is a major topic as far as assistive technologies are concerned. This is due to the high demand for the products and technologies related to fall detection with the ageing population around the globe. This paper gives a review of previous works on human fall detection devices and a preliminary results from a developing depth sensor based device. The three main approaches used in fall detection devices such as wearable based devices, ambient based devices and vision based devices are identified along with the sensors employed.  The frameworks and algorithms applied in each of the approaches and their uniqueness is also illustrated. After studying the performance and the shortcoming of the available systems a future solution using depth sensor is also proposed with preliminary results

Development of Human Fall Detection System using Joint Height, Joint Velocity, and Joint Position from Depth Maps

Human falls are a major health concern in many communities in today’s aging population. There are different approaches used in developing fall detection system such as some sort of wearable, ambient sensor and vision based systems. This paper proposes a vision based human fall detection system using Kinect for Windows. The generated depth stream from the sensor is used in the proposed algorithm to differentiate human fall from other activities based on human Joint height, joint velocity and joint positions. From the experimental results our system was able to achieve an average accuracy of 96.55% with a sensitivity of 100% and specificity of 95

A Novel Algorithm for Human Fall Detection using Height, Velocity and Position of the Subject from Depth Maps

Human fall detection systems play an important role in our daily life, because falls are the main obstacle for elderly people to live independently and it is also a major health concern due to aging population. Different approaches are used to develop human fall detection systems for elderly and people with special needs. The three basic approaches include some sort of wearable devices, ambient based devices or non-invasive vision-based devices using live cameras. Most of such systems are either based on wearable or ambient sensor which is very often rejected by users due to the high false alarm and difficulties in carrying them during their daily life activities. This paper proposes a fall detection system based on the height, velocity and position of the subject using depth information from Microsoft Kinect sensor. Classification of human fall from other activities of daily life is accomplished using height and velocity of the subject extracted from the depth information. Finally position of the subject is identified for fall confirmation. From the experimental results, the proposed system was able to achieve an average accuracy of 94.81% with sensitivity of 100% and specificity of 93.33%

Development of a portable Muslim prayer time table clock

The five daily Muslim prayer times namely Fajr, Zuhr, Asr, Maghrib and Isha vary from place to place and from day to day. The timings of these five prayers are not even for locations with same time zones. The exact timing of each of the prayer is important, because it is obligatory for every Muslim to perform these prayers at the correct time. The prayer times for any given location can be mathematically determined if certain parameters such as the coordinates of the location are known. The mathematical calculation become lengthy and tedious when the calculation of all the prayer times are taken into account, but the algorithms can be implemented into computers, microprocessor or on microcontroller. This project is about finding and implementing algorithms required for calculating the accurate five daily Muslim prayer times on ARM7 LPC2138 microcontroller and display the prayer times using 7 segments. Most importantly allowing the users to change the location information and other parameters used for prayer time calculation which describes different figh rules (conventions from major Islamic Organizations) and difference of opinion (in Mazhab) for Asr prayer time. Thus making it a flexible portable Muslim Prayer time clock which can be used almost anywhere in the globe, catering for the minor differing in schools of Islamic thoughts

Biomechanical application: exploitation of kinect sensor

Human gait recognition is an important indicator and are extensively studied research area especially with the aging population and rehabilitation applications. Application of gait analysis ranges from diagnosis, monitoring and early detection of potential hazards such as human fall. There are various types of approaches used in gait analysis including wearable, ambient and vision based devices. Microsoft Kinect sensor is well-known among researchers since it can give depth and normal colour images as well. This paper presents a preliminary study on gait analysis of lower body parts. The measurement taken includes step width, step lengths, stride lengths and angles of knee respective hip and ankle while walking. The results showed that the algorithms implemented were able to accurately measure the lengths with low error rate

Human fall detection from depth images using position and velocity of subject

Fall detection and notification systems play an important role in our daily life, since human fall is a major health concern for many communities in today's aging population. There are different approaches used in developing human fall detection systems for elderly and people with special needs such as disable. The three basic approaches include some sort of wearable, non-wearable ambient sensor and vision based systems. This paper proposes a human fall detection system based on the velocity and position of the subject, extracted from Microsoft Kinect Sensor. Initially the subject and floor plane are extracted and tracked frame by frame. The tracked joints of the subject are then used to measure the velocity with respect to the previous location. Fall detection is confirmed using the position of the subject to see if all the joints are on the floor after an abnormal velocity. From the experimental results obtained, our system was able to achieve an average accuracy of 93.94% with a sensitivity of 100% and specificity of 91.3%

Powering an island energy system by offshore floating technologies towards 100% renewables: A case for the Maldives

Low-lying coastal areas and archipelago countries are particularly threatened by the impacts of climate change. Concurrently, many island states still rely on extensive use of imported fossil fuels, above all diesel for electricity generation, in addition to hydrocarbon-based fuels to supply aviation and marine transportation. Land area is usually scarce and conventional renewable energy solutions cannot be deployed in a sufficient way. This research highlights the possibility of floating offshore technologies being able to fulfil the task of replacing fossil fuels with renewable energy solutions in challenging topographical areas. On the case of the Maldives, floating offshore solar photovoltaics, wave power and offshore wind are modelled on a full hourly resolution in two different scenarios to deal with the need of transportation fuels: By importing the necessary, carbon neutral synthetic e-fuels from the world market, or by setting up local production capacities for e-fuels. Presented results show that a fully renewable energy system is technically feasible in 2030 with a relative cost per final energy of 120.3 €/MWh and 132.1 €/MWh, respectively, for the two scenarios in comparison to 105.7 €/MWh of the reference scenario in 2017. By 2050, cost per final energy can be reduced to 77.6 €/MWh and 92.6 €/MWh, respectively. It is concluded that floating solar photovoltaics and wave energy converters will play an important role in defossilisation of islands and countries with restricted land area