Use of kinematic and mel-cepstrum-related features for fall detection based on data from infrared depth sensors

Methods for Spatiotemporal Analysis of Human Gait Based on Data from Depth Sensors

Gait analysis may serve various purposes related to health care, such as the estimation of elderly people's risk of falling. This paper is devoted to gait analysis based on data from depth sensors which are suitable for use both at healthcare facilities and in monitoring systems dedicated to household environments. This paper is focused on the comparison of three methods for spatiotemporal gait analysis based on data from depth sensors, involving the analysis of the movement trajectories of the knees, feet, and centre of mass. The accuracy of the results obtained using those methods was assessed for different depth sensors' viewing angles and different types of subject clothing. Data were collected using a Kinect v2 device. Five people took part in the experiments. Data from a Zebris FDM platform were used as a reference. The obtained results indicate that the viewing angle and the subject's clothing affect the uncertainty of the estimates of spatiotemporal gait parameters, and that the method based on the trajectories of the feet yields the most information, while the method based on the trajectory of the centre of mass is the most robust.

A Non-Contact Fall Detection Method for Bathroom Application Based on MEMS Infrared Sensors

The ratio of the elderly to the total population around the world is larger than 10%, and about 30% of the elderly are injured by falls each year. Accidental falls, especially bathroom falls, account for a large proportion. Therefore, fall events detection of the elderly is of great importance. In this article, a non-contact fall detector based on a Micro-electromechanical Systems Pyroelectric Infrared (MEMS PIR) sensor and a thermopile IR array sensor is designed to detect bathroom falls. Besides, image processing algorithms with a low pass filter and double boundary scans are put forward in detail. Then, the statistical features of the area, center, duration and temperature are extracted. Finally, a 3-layer BP neural network is adopted to identify the fall events. Taking into account the key factors of ambient temperature, objective, illumination, fall speed, fall state, fall area and fall scene, 640 tests were performed in total, and 5-fold cross validation is adopted. Experimental results demonstrate that the averages of the precision, recall, detection accuracy and F₁-Score are measured to be 94.45%, 90.94%, 92.81% and 92.66%, respectively, which indicates that the novel detection method is feasible. Thereby, this IOT detector can be extensively used for household bathroom fall detection and is low-cost and privacy-security guaranteed.

In-Home Older Adults' Activity Pattern Monitoring Using Depth Sensors: A Review

The global population is aging due to many factors, including longer life expectancy through better healthcare, changing diet, physical activity, etc. We are also witnessing various frequent epidemics as well as pandemics. The existing healthcare system has failed to deliver the care and support needed to our older adults (seniors) during these frequent outbreaks. Sophisticated sensor-based in-home care systems may offer an effective solution to this global crisis. The monitoring system is the key component of any in-home care system. The evidence indicates that they are more useful when implemented in a non-intrusive manner through different visual and audio sensors. Artificial Intelligence (AI) and Computer Vision (CV) techniques may be ideal for this purpose. Since the RGB imagery-based CV technique may compromise privacy, people often hesitate to utilize in-home care systems which use this technology. Depth, thermal, and audio-based CV techniques could be meaningful substitutes here. Due to the need to monitor larger areas, this review article presents a systematic discussion on the state-of-the-art using depth sensors as primary data-capturing techniques. We mainly focused on fall detection and other health-related physical patterns. As gait parameters may help to detect these activities, we also considered depth sensor-based gait parameters separately. The article provides discussions on the topic in relation to the terminology, reviews, a survey of popular datasets, and future scopes.

Health technologies for the prevention and detection of falls in adult hospital inpatients: a scoping review

OBJECTIVE

The objective of this scoping review was to examine and map the evidence relating to the reporting and evaluation of technologies for the prevention and detection of falls in adult hospital inpatients.

INTRODUCTION

Falls are a common cause of accidental injury, leading to significant safety issues in hospitals globally, and resulting in substantial human and economic costs. Previous research has focused on community settings with less emphasis on hospital settings.

INCLUSION CRITERIA

Participants included adult inpatients, aged 18 years and over; the concept included the use of fall-prevention or fall-detection technologies; the context included any hospital ward setting.

METHODS

This scoping review was conducted according to JBI methodology for scoping reviews, guided by an a priori protocol. A wide selection of databases including MEDLINE, CINAHL, AMED, Embase, PEDro, Epistimonikos, and Science Direct were searched for records from inception to October 2019. Other sources included gray literature, trial registers, government health department websites, and websites of professional bodies. Only studies in the English language were included. A three-step search strategy was employed, with all records exported for subsequent title and abstract screening prior to full-text screening. Screening was performed by two independent reviewers and data extraction by one reviewer following agreement checks. Data are presented in narrative and tabular form.

RESULTS

Over 13,000 records were identified with 404 included in the scoping review: 336 reported on fall-prevention technologies, 51 targeted detection, and 17 concerned both. The largest contributions of studies came from the USA (n=185), Australia (n=65), the UK (n=36), and Canada (n=18). There was a variety of study designs including 77 prospective cohort studies, 33 before-after studies, and 35 systematic reviews; however, relatively few randomized controlled trials were conducted (n = 25). The majority of records reported on multifactorial and multicomponent technologies (n = 178), followed by fall detection devices (n = 86). Few studies reported on the following interventions in isolation: fall risk assessment (n = 6), environment design (n = 8), sitters (n = 5), rounding (n = 3), exercise (n = 3), medical/pharmaceutical (n = 2), physiotherapy (n = 1), and nutritional (n = 1). The majority (57%) of studies reported clinical effectiveness outcomes, with smaller numbers (14%) reporting feasibility and/or acceptability outcomes, or cost-effectiveness outcomes (5%).

CONCLUSIONS

This review has mapped the literature on fall-prevention and fall-detection technology and outcomes for adults in the hospital setting. Despite the volume of available literature, there remains a need for further high-quality research on fall-prevention and fall-detection technologies.

CNN-Based Fall Detection Strategy with Edge Computing Scheduling in Smart Cities

The livelihood problem, especially the medical wisdom, has played an important role during the process of the building of smart cities. For the medical wisdom, the fall detection has attracted the considerable attention from the global researchers and medical institutions. It is very difficult for the traditional fall detection strategies to realize the intelligent detection with the following three reasons: (i) the data collection cannot reach the real-time level; (ii) the adopted detection methods cannot satisfy the enough stability; and (iii) the computation overhead of collection device is very high, which causes the barely satisfactory detection effect. Therefore, this paper proposes Convolutional Neural Network (CNN)-based fall detection strategy with edge computing consideration, where the global network view ability of Software-Defined Networking (SDN) is used to collect the generated data from smartphone. Meanwhile, on one hand, the edge computing is exploited to put some computation tasks at the edge server by the scheduling technique. On the other hand, CNN is equipped with both edge server and smartphone, and it is leveraged to train the related data and further give the guidance of fall detection. The experimental results show that the novel fall detection strategy has a more accurate rate, transmission delay, and stability than two cutting-edge strategies.

Semi-Automatic Calibration Method for a Bed-Monitoring System Using Infrared Image Depth Sensors

With the aging of society, the number of fall accidents has increased in hospitals and care facilities, and some accidents have happened around beds. To help prevent accidents, mats and clip sensors have been used in these facilities but they can be invasive, and their purpose may be misinterpreted. In recent years, research has been conducted using an infrared-image depth sensor as a bed-monitoring system for detecting a patient getting up, exiting the bed, and/or falling; however, some manual calibration was required initially to set up the sensor in each instance. We propose a bed-monitoring system that retains the infrared-image depth sensors but uses semi-automatic rather than manual calibration in each situation where it is applied. Our automated methods robustly calculate the bed region, surrounding floor, sensor location, and attitude, and can recognize the spatial position of the patient even when the sensor is attached but unconstrained. Also, we propose a means to reconfigure the spatial position considering occlusion by parts of the bed and also accounting for the gravity center of the patient’s body. Experimental results of multi-view calibration and motion simulation showed that our methods were effective for recognition of the spatial position of the patient.