Data Mining Practical Machine Learning Tools and Techniques

Automatic fall detection is a very active research area, which has grown explosively since the 2010s, especially focused on elderly care. Rapid detection of falls favors early awareness from the injured person, reducing a series of negative consequences in the health of the elderly. Currently, there are several fall detection systems (FDSs), mostly based on predictive and machine-learning approaches. These algorithms are based on different data sources, such as wearable devices, ambient-based sensors, or vision/camera-based approaches. While wearable devices like inertial measurement units (IMUs) and smartphones entail a dependence on their use, most image-based devices like Kinect sensors generate video recordings, which may affect the privacy of the user. Regardless of the device used, most of these FDSs have been tested only in controlled laboratory environments, and there are still no mass commercial FDS. The latter is partly due to the impossibility of counting, for ethical reasons, with datasets generated by falls of real older adults. All public datasets generated in laboratory are performed by young people, without considering the differences in acceleration and falling features of older adults. Given the above, this article presents the eHomeSeniors dataset, a new public dataset which is innovative in at least three aspects: first, it collects data from two different privacy-friendly infrared thermal sensors; second, it is constructed by two types of volunteers: normal young people (as usual) and performing artists, with the latter group assisted by a physiotherapist to emulate the real fall conditions of older adults; and third, the types of falls selected are the result of a thorough literature review.

https://www.mdpi.com/1424-8220/19/20/4565/pdf

eHomeSeniors Dataset: An Infrared Thermal Sensor Dataset for Automatic Fall Detection Research.

/pdf/pathway-of-trends-and-technologies-in-fall-detection-a-rn19gthd.pdf

Pathway of Trends and Technologies in Fall Detection: A Systematic Review

Gait velocity has been consistently shown to be an important indicator and predictor of health status, especially in older adults. It is often assessed clinically, but the assessments occur infrequently and do not allow optimal detection of key health changes when they occur. In this paper, we show that the time gap between activations of a pair of Passive Infrared (PIR) motion sensors installed in the consecutively visited room pair carry rich latent information about a person's gait velocity. We name this time gap transition time and show that despite a six second refractory period of the PIR sensors, transition time can be used to obtain an accurate representation of gait velocity. 
Using a Support Vector Regression (SVR) approach to model the relationship between transition time and gait velocity, we show that gait velocity can be estimated with an average error less than 2.5 cm/sec. This is demonstrated with data collected over a 5 year period from 74 older adults monitored in their own homes. 
This method is simple and cost effective and has advantages over competing approaches such as: obtaining 20 to 100x more gait velocity measurements per day and offering the fusion of location-specific information with time stamped gait estimates. These advantages allow stable estimates of gait parameters (maximum or average speed, variability) at shorter time scales than current approaches. This also provides a pervasive in-home method for context-aware gait velocity sensing that allows for monitoring of gait trajectories in space and time.

Gait Velocity Estimation using time interleaved between Consecutive Passive IR Sensor Activations

Ambient, non-intrusive approaches to smart home health monitoring, while limited in capability, are preferred by residents. More intrusive methods of sensing, such as video and wearables, can offer richer data but at the cost of lower resident uptake, in part due to privacy concerns. A radio frequency-based approach to sensing, Channel State Information (CSI), can make use of low cost off-the-shelf WiFi hardware. We have implemented an activity recognition system on the Raspberry Pi 4, one of the world's most popular embedded boards. We have implemented an classification system using the Pi to demonstrate its capability for activity recognition. This involves performing data collection, interpretation and windowing, before supplying the data to a classification model. In this paper, the capabilities of the Raspberry Pi 4 at performing activity recognition on CSI data are investigated. We have developed and publicly released a data interaction framework, capable of interpreting, processing and visualising data from a range of CSI-capable hardware. Furthermore, CSI data captured for these experiments during various activity performances have also been made publicly available. We then train a Deep Convolutional LSTM model to classify the activities. Our experiments, performed in a small apartment, achieve 92% average accuracy on 11 activity classes.

/pdf/wifi-based-human-activity-recognition-using-raspberry-pi-3cpvj0dw89.pdf

WiFi-based Human Activity Recognition using Raspberry Pi

The number of methods for identifying potential fall risk is growing as the rate of elderly fallers continues to rise in the UK. Assessments for identifying risk of falling are usually performed in hospitals and other laboratory environments, however these are costly and cause inconvenience for the subject and health services. Replacing these intrusive testing methods with a passive in-home monitoring solution would provide a less time-consuming and cheaper alternative. As sensors become more readily available, machine learning models can be applied to the large amount of data they produce. This can support activity recognition, falls detection, prediction and risk determination. In this review, the growing complexity of sensor data, the required analysis, and the machine learning techniques used to determine risk of falling are explored. The current research on using passive monitoring in the home is discussed, while the viability of active monitoring using vision-based and wearable sensors is considered. Methods of fall detection, prediction and risk determination are then compared.

/pdf/fall-prediction-using-behavioural-modelling-from-sensor-data-2gdl5dxni8.pdf

Fall prediction using behavioural modelling from sensor data in smart homes.

As people live longer, the increasing average age of the population places additional strains on our health and social services. There are widely recognised benefits to both the individual and society from supporting people to live independently for longer in their own homes. However, falls in particular have been found to be a leading cause of the elderly moving into care, and yet surprisingly preventative approaches are not in place; fall detection and rehabilitation are too late. In this paper we present FITsense, which is building a Smart Home environment to identify increased risk of falls for residents, and so allow timely interventions before falls occurs. An ambient sensor network, installed in the Smart Home, identifies low level events taking place which is analysed to generate a resident’s profile of activities of daily living (ADLs). These ADL profiles are compared to both the resident’s typical profile and to known “risky” profiles to allow evidence-driven intervention recommendations. Human activity recognition to identify ADLs from sensor data is a key challenge. Here we compare a windowing-based and a sequence-based event representation on four existing datasets. We find that windowing works well, giving consistent performance but may lack sufficient granularity for more complex multi-part activities.

/pdf/fitsense-employing-multi-modal-sensors-in-smart-homes-to-4xmguojhfz.pdf

FITsense: employing multi-modal sensors in smart homes to predict falls.

We consider use of an ambient sensor network, installed in Smart Homes, to identify low level events taking place which can then be analysed to generate a resident's profile of activities of daily living (ADLs). These ADL profiles are compared to both the resident's typical profile and to known 'risky' profiles to support evidence-based interventions. Human activity recognition to identify ADLs from sensor data is a key challenge, a windowbased representation is compared on four existing datasets. We find that windowing works well, giving consistent performance. We also introduce FITsense, which is building a Smart Home environment to specifically identify increased risk of falls to allow interventions before falls occurs.

/pdf/monitoring-health-in-smart-homes-using-simple-sensors-1u3h272c2g.pdf

Monitoring health in smart homes using simple sensors.

Long term health conditions, such as fall risk, are traditionally diagnosed through testing performed in hospital environments. Smart Homes offer the opportunity to perform continuous, long-term behavioural and vitals monitoring of residents, which may be employed to aid diagnosis and management of chronic conditions without placing additional strain on health services. A profile of the resident’s behaviour can be produced from sensor data, and then compared over time. Activity Recognition is a primary challenge for profile generation, however many of the approaches adopted fail to take full advantage of the inherent temporal dependencies that exist in the activities taking place. Long Short Term Memory (LSTM) is a form of recurrent neural network that uses previously learned examples to inform classification decisions. In this paper we present a variety of approaches to human activity recognition using LSTMs which consider the temporal dependencies present in the sensor data in order to produce richer representations and improved classification accuracy. The LSTM approaches are compared to the performance of a selection of baseline classification algorithms on several real world datasets. In general, it was found that accuracy in LSTMs improved as additional temporal information was presented to the classifier.

/pdf/representing-temporal-dependencies-in-smart-home-activity-2d8ovcr6d3.pdf

Glenn Forbes

Papers

Fall prediction using behavioural modelling from sensor data in smart homes.

WiFi-based Human Activity Recognition using Raspberry Pi

FITsense: employing multi-modal sensors in smart homes to predict falls.

Monitoring health in smart homes using simple sensors.

Representing Temporal Dependencies in Smart Home Activity Recognition for Health Monitoring