scholarly journals Ans-Assist: Robust Human Fall Detection for Unconstraint Smartphone Positions using Modified Long Short-Term Memory Cell

2019 ◽  
Vol 8 (4) ◽  
pp. 5659-5663
Keyword(s):  
Short Term Memory ◽  
Population Distribution ◽  
Elderly Person ◽  
Memory Cell ◽  
Fall Detection ◽  
Automatic Monitoring ◽  
Short Term ◽  
Term Memory ◽  
Robust Model ◽  
Long Short Term Memory

In many aging countries, where the population distribution has shifted to old ages, the need for automatic monitoring devices to help an elderly person when they fall is very crucial. Smartphone is one of the best candidate devices for detecting fall because accelerometer and gyroscope sensors embedded in it respond based on human movements. People usually carry their smartphone in any position and can make fall detection method difficult to detect when fall occurs. This research explored the model for unconstraint human fall detection by using the sensors embedded in smartphone for carried/wearable- sensor-based method. We proposed robust model called Ans-Assist using modified cell of Long Short-Term Memory based model as fall recognition model which can detect human fall from any smartphone position (unconstraint). Some experimental results showed that Ans-Assist achieved 0.95 (± 0.028) average accuracy value using unconstraint smartphone positions. This model can adapt the input from accelerometer and gyroscope sensors which are responsive when human fall.

Automatic Fall Detection Using Long Short-Term Memory Network

2021 ◽  
pp. 359-371
Author(s):  
Carlos Magalhães ◽  
João Ribeiro ◽  
Argentina Leite ◽  
E. J. Solteiro Pires ◽  
João Pavão
Keyword(s):  
Short Term Memory ◽  
Fall Detection ◽  
Short Term ◽  
Term Memory ◽  
Memory Network ◽  
Long Short Term Memory

Integrating Geospatial Data and Social Media in Bidirectional Long-Short Term Memory Models to Capture Human Nature Interactions

2020 ◽  
Author(s):  
Andrew Larkin ◽  
Perry Hystad
Keyword(s):  
New York ◽  
New York City ◽  
Stress Reduction ◽  
Short Term Memory ◽  
Memory Cell ◽  
Memory Model ◽  
Urban Nature ◽  
Short Term ◽  
Term Memory ◽  
Long Short Term Memory

Abstract Contact with nature has been linked to human health, but little information is available for how individuals utilize urban nature. We developed a bidirectional long short-term memory model for classifying whether tweets describe the proposed pathways through which nature influences health: exercise, aesthetic stimulation, stress reduction, safety, air pollution mediation, and/or social interaction. To adjust for regional variations in urban nature context, we integrated OpenStreetMap data on nature and non-nature features for each long-short term memory cell. Training (n = 63073), development (n = 5000), and test (n = 5000) sets consisted of labeled tweets from Portland, Oregon. Tweets from New York City (NYC) (n = 5000) were also labeled to test generalizability. The model was applied retrospectively to 20 million tweets from 2017 and continuously to Meetup posts for 7,708 cities in North America. F1Scores ranged from 0.54 to 0.82 in the NYC dataset, a 24% to 92% improvement over current methods. Precision ranged from 0.58 to 0.83, while recall ranged from 0.39 to 0.81. Adding OpenStreetMap features led to greater percent and absolute F1Scores in NYC compared to Portland. Average F1Scores were greater in models with a nature label in addition to human behavior labels (0.59 vs. 0.65), suggesting health behaviors are influenced by urban nature.

scholarly journals Room-Level Fall Detection Based on Ultra-Wideband (UWB) Monostatic Radar and Convolutional Long Short-Term Memory (LSTM)

Sensors ◽  
2020 ◽  
Vol 20 (4) ◽  
pp. 1105
Author(s):  
Liang Ma ◽  
Meng Liu ◽  
Na Wang ◽  
Lu Wang ◽  
Yang Yang ◽  
...  
Keyword(s):  
Neural Network ◽  
Privacy Protection ◽  
Short Term Memory ◽  
Fall Detection ◽  
Impulse Radio ◽  
Ultra Wideband ◽  
Short Term ◽  
Term Memory ◽  
Long Short Term Memory ◽  
Training Effort

Timely calls for help can really make a difference for elders who suffer from falls, particularly in private locations. Considering privacy protection and convenience for the users, in this paper, we approach the problem by using impulse–radio ultra-wideband (IR-UWB) monostatic radar and propose a learning model that combines convolutional layers and convolutional long short term memory (ConvLSTM) to extract robust spatiotemporal features for fall detection. The performance of the proposed scheme was evaluated in terms of accuracy, sensitivity, and specificity. The results show that the proposed method outperforms convolutional neural network (CNN)-based methods. Of the six activities we investigated, the proposed method can achieve a sensitivity of 95% and a specificity of 92.6% at a range of 8 meters. Further tests in a heavily furnished lounge environment showed that the model can detect falls with more than 90% sensitivity, even without re-training effort. The proposed method can detect falls without exposing the identity of the users. Thus, the proposed method is ideal for room-level fall detection in privacy-prioritized scenarios.

scholarly journals User Presence Inference via Encrypted Traffic of Wireless Camera in Smart Homes

2018 ◽  
Vol 2018 ◽  
pp. 1-10 ◽  
Author(s):  
Xiaoyu Ji ◽  
Yushi Cheng ◽  
Wenyuan Xu ◽  
Xinyan Zhou
Keyword(s):  
Short Term Memory ◽  
Smart Homes ◽  
Fall Detection ◽  
Personal Privacy ◽  
Short Term ◽  
Term Memory ◽  
Long Short Term Memory ◽  
Lstm Network ◽  
Cumulative Sum Control ◽  
Encrypted Traffic

Wireless cameras are widely deployed in smart homes for security guarding, baby monitoring, fall detection, and so on. Those security cameras, which are supposed to protect users, however, may in turn leak a user’s personal privacy. In this paper, we reveal that attackers are able to infer whether users are at home or not, that is, the user presence, by eavesdropping the traffic of wireless cameras from distance. We propose HomeSpy, a system that infers user presence by inspecting the intrinsic pattern of the wireless camera traffic. To infer the user presence, HomeSpy first eavesdrops the wireless traffic around the target house and detects the existence of wireless cameras with a Long Short-Term Memory (LSTM) network. Then, HomeSpy infers the user presence using the bitrate variation of the wireless camera traffic based on a cumulative sum control chart (CUSUM) algorithm. We implement HomeSpy on the Android platform and validate it on 20 cameras. The evaluation results show that HomeSpy can achieve a successful attack rate of 97.2%.

scholarly journals Abstract: Multi-camera, Multi-person, and Real-time Fall Detection using Long Short Term Memory

2021 ◽  
pp. 124-124
Author(s):  
Christian Heinrich ◽  
Samad Koita ◽  
Mohammad Taufeeque ◽  
Nicolai Spicher ◽  
Thomas M. Deserno
Keyword(s):  
Real Time ◽  
Short Term Memory ◽  
Fall Detection ◽  
Short Term ◽  
Term Memory ◽  
Long Short Term Memory ◽  
Time Fall
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