Smartphone Based Data Mining for Fall Detection: Analysis and Design

Hakim, Abdul; Huq, M. S.; Shanta, Shahnoor; Ibrahim, Badaruddin

doi:10.1016/j.procs.2017.01.188

Cited by 82 publications

(49 citation statements)

References 17 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…The study of the prototype presented by Astriani et al in [30] suggests that the combined use of accelerometry and gyroscope signals in basic threshold-methods seems to improve the accuracy of the detector, although the system was evaluated with a small set of falls (only 84). Similarly, according to the results presented by Hakim et al in [46], the accuracy of a smartphone-based fall detector (using different machine learning strategies) augments as the number of considered IMU sensors increases. However, only six experimental subjects (and a very small sampling rate of 10 Hz) were employed to generate the training samples of the detection methods.…”

Section: Related Workmentioning

confidence: 61%

A Study of the Use of Gyroscope Measurements in Wearable Fall Detection Systems

2020

View full text Add to dashboard Cite

Due to the serious impact of falls on the quality of life of the elderly and on the economical sustainability of health systems, the study of new monitoring systems capable of automatically alerting about falls has gained much research interest during the last decade. In the field of Human Activity Recognition, Fall Detection Systems (FDSs) can be contemplated as pattern recognition architectures able to discriminate falls from ordinary Activities of Daily Living (ADLs). In this regard, the combined application of cellular communications and wearable devices that integrate inertial sensors offers a cost-efficient solution to track the user mobility almost ubiquitously. Inertial Measurement Units (IMUs) typically utilized for these architectures, embed an accelerometer and a gyroscope. This paper investigates if the use of the angular velocity (captured by the gyroscope) as an input feature of the movement classifier introduces any benefit with respect to the most common case in which the classification decision is uniquely based on the accelerometry signals. For this purpose, the work assesses the performance of a deep learning architecture (a convolutional neural network) which is optimized to differentiate falls from ADLs as a function of the raw data measured by the two inertial sensors (gyroscope and accelerometer). The system is evaluated against on a well-known public dataset with a high number of mobility traces (falls and ADL) measured from the movements of a wide group of experimental users.

show abstract

Section: Related Workmentioning

confidence: 61%

A Study of the Use of Gyroscope Measurements in Wearable Fall Detection Systems

2020

View full text Add to dashboard Cite

show abstract

“…Future work on fall detection devices can incorporate SRS for identifying joint kinematics. Moreover, wireless sensor networks, algorithms, and machine learning techniques have been used along with accelerometers and IMUs for fall detection [6,[8][9][10]39] and in the future can also be implemented using SRS. However, adding electromyography (EMG) for fall detection in addition to joint kinematics detection can increase the accuracy of pre-impact fall detection, using both biomechanical and neuromuscular measures.…”

Section: Future Workmentioning

confidence: 99%

Closing the Wearable Gap—Part III: Use of Stretch Sensors in Detecting Ankle Joint Kinematics During Unexpected and Expected Slip and Trip Perturbations

et al. 2019

View full text Add to dashboard Cite

Background: An induced loss of balance resulting from a postural perturbation has been reported as the primary source for postural instability leading to falls. Hence; early detection of postural instability with novel wearable sensor-based measures may aid in reducing falls and fall-related injuries. The purpose of the study was to validate the use of a stretchable soft robotic sensor (SRS) to detect ankle joint kinematics during both unexpected and expected slip and trip perturbations. Methods: Ten participants (age: 23.7 ± 3.13 years; height: 170.47 ± 8.21 cm; mass: 82.86 ± 23.4 kg) experienced a counterbalanced exposure of an unexpected slip, an unexpected trip, an expected slip, and an expected trip using treadmill perturbations. Ankle joint kinematics for dorsiflexion and plantarflexion were quantified using three-dimensional (3D) motion capture through changes in ankle joint range of motion and using the SRS through changes in capacitance when stretched due to ankle movements during the perturbations. Results: A greater R-squared and lower root mean square error in the linear regression model was observed in comparing ankle joint kinematics data from motion capture with stretch sensors. Conclusions: Results from the study demonstrated that 71.25% of the trials exhibited a minimal error of less than 4.0 degrees difference from the motion capture system and a greater than 0.60 R-squared value in the linear model; suggesting a moderate to high accuracy and minimal errors in comparing SRS to a motion capture system. Findings indicate that the stretch sensors could be a feasible option in detecting ankle joint kinematics during slips and trips.

show abstract

“…La Concepcion et al 41 leverage data retrieved from accelerometer sensors to generate discrete variables, then the core of the algorithm Ameva is used to develop the selection, discretization, and classification technique for activity recognition. Hakim et al 42 propose a threshold-based fall detection algorithm using a supervised machine learning algorithm to classify activities of daily living (ADL). However, all these attitude detection methods focus on the health and safety implication of individuals rather than improving the accuracy of a positioning system.…”

Section: Related Workmentioning

confidence: 99%

Toward improving indoor magnetic field–based positioning system using pedestrian motion models

Shao

Luo

Zhao

et al. 2018

International Journal of Distributed Sensor Networks

View full text Add to dashboard Cite

Indoor magnetic field has attracted considerable attention in indoor location-based services, because of its pervasive and stable attributes. Generally, in order to harness the location features of the magnetic field, particle filters are introduced to simulate the possibilities of user locations. Real-time magnetic field fingerprints are matched with model fingerprints to adjust the location possibilities. However, the computation overheads of the magnetic matching are rather high, thus limiting their applications to mobile computing platforms and indoor location-based service providers that serve massive users. In order to reduce the computation overhead, the article presents a low-cost magnetic field fingerprint matching scheme. Based on the low-frequency features of the magnetic field, the scheme updates particle weights according to the mass center of the magnetic field deltas of pedestrian steps. The proposed low-cost scheme decreases the complexity of real-time fingerprints without harming the positioning performance. In order to further improve the positioning accuracy, not asking users to hold the smartphone in fixed attitudes, we also present a smartphone attitude detection method that enables the proposed scheme to automatically select proper fingerprints. Experiments convincingly reveal that the proposed scheme achieves about 1 m accuracy at 80% with low computation overheads.

show abstract

Smartphone Based Data Mining for Fall Detection: Analysis and Design

Cited by 82 publications

References 17 publications

A Study of the Use of Gyroscope Measurements in Wearable Fall Detection Systems

A Study of the Use of Gyroscope Measurements in Wearable Fall Detection Systems

Closing the Wearable Gap—Part III: Use of Stretch Sensors in Detecting Ankle Joint Kinematics During Unexpected and Expected Slip and Trip Perturbations

Toward improving indoor magnetic field–based positioning system using pedestrian motion models

Contact Info

Product

Resources

About