Learning the Orientation of a Loosely-Fixed Wearable IMU Relative to the Body Improves the Recognition Rate of Human Postures and Activities

Re-Orienting Smartphone-Collected Car Motion Data Using Least-Squares Estimation and Machine Learning

Smartphone sensors can collect data in many different contexts. They make it feasible to obtain large amounts of data at little or no cost because most people own mobile phones. In this work, we focus on collecting motion data in the car using a smartphone. Motion sensors, such as accelerometers and gyroscopes, can help obtain information about the vehicle’s dynamics. However, the different positioning of the smartphone in the car leads to difficulty interpreting the sensed data due to an unknown orientation, making the collection useless. Thus, we propose an approach to automatically re-orient smartphone data collected in the car to a standardized orientation (i.e., with zero yaw, roll, and pitch angles with respect to the vehicle). We use a combination of a least-square plane approximation and a Machine Learning model to infer the relative orientation angles. Then we populate rotation matrices and perform the data rotation. We trained the model by collecting data using a vehicle physics simulator.

Assessment of postural sway with a pendant-mounted wearable sensor

BACKGROUND

Body-worn inertial measurement unit (IMU) sensors have been widely used in postural stability and balance studies because of their low cost and convenience. In most of these studies, a single IMU sensor is attached to a waist belt near the body's center of mass. Some populations such as pregnant women, however, may find a waist belt challenging in terms of fit and comfort. For this reason it may be useful to identify an alternative location for placement of an IMU and a more comfortable means for attaching the sensor to the body. Research question Does placing an IMU sensor in a pendant worn around the neck permit discrimination between conditions with varying postural stability?

METHODS

Twenty-six healthy participants performed three standing tasks (double-leg, tandem, and single-leg standing) under eyes-open and eyes-closed vision conditions to preliminarily assess the ability of the pendant sensor to discriminate between balance conditions. Discrimination based upon data from a belt-mounted IMU was assessed in the same trials. Differences in standard deviation of acceleration components, sway area, and jerkiness due to trial condition and sensor were evaluated using analysis of variance followed by post hoc comparisons. These data were also incorporated into receiver-operator characteristic (ROC) curve analysis to assess the effectiveness of each sensor at discriminating between conditions.

RESULTS

Stability was found to vary across conditions, but there was no interaction between stability and sensor location (all p ≥ 0.323). ROC curve analysis showed that sensors in both locations were good discriminators between conditions. Significance Placing an IMU in a pendant may be feasible for studying and monitoring postural instability. This approach may be especially valuable when considering populations for which wearing a belt is uncomfortable.

A systematic review of smartphone-based human activity recognition methods for health research

Smartphones are now nearly ubiquitous; their numerous built-in sensors enable continuous measurement of activities of daily living, making them especially well-suited for health research. Researchers have proposed various human activity recognition (HAR) systems aimed at translating measurements from smartphones into various types of physical activity. In this review, we summarized the existing approaches to smartphone-based HAR. For this purpose, we systematically searched Scopus, PubMed, and Web of Science for peer-reviewed articles published up to December 2020 on the use of smartphones for HAR. We extracted information on smartphone body location, sensors, and physical activity types studied and the data transformation techniques and classification schemes used for activity recognition. Consequently, we identified 108 articles and described the various approaches used for data acquisition, data preprocessing, feature extraction, and activity classification, identifying the most common practices, and their alternatives. We conclude that smartphones are well-suited for HAR research in the health sciences. For population-level impact, future studies should focus on improving the quality of collected data, address missing data, incorporate more diverse participants and activities, relax requirements about phone placement, provide more complete documentation on study participants, and share the source code of the implemented methods and algorithms.

Wearable and Nearable Biosensors and Systems for Healthcare

Biosensors and systems in the form of wearables and "nearables" (i [...].

Challenges of machine learning model validation using correlated behaviour data: Evaluation of cross-validation strategies and accuracy measures

Automated monitoring of the movements and behaviour of animals is a valuable research tool. Recently, machine learning tools were applied to many species to classify units of behaviour. For the monitoring of wild species, collecting enough data for training models might be problematic, thus we examine how machine learning models trained on one species can be applied to another closely related species with similar behavioural conformation. We contrast two ways to calculate accuracies, termed here as overall and threshold accuracy, because the field has yet to define solid standards for reporting and measuring classification performances. We measure 21 dogs and 7 wolves, and find that overall accuracies are between 51 and 60% for classifying 8 behaviours (lay, sit, stand, walk, trot, run, eat, drink) when training and testing data are from the same species and between 41 and 51% when training and testing is cross-species. We show that using data from dogs to predict the behaviour of wolves is feasible. We also show that optimising the model for overall accuracy leads to similar overall and threshold accuracies, while optimizing for threshold accuracy leads to threshold accuracies well above 80%, but yielding very low overall accuracies, often below the chance level. Moreover, we show that the most common method for dividing the data between training and testing data (random selection of test data) overestimates the accuracy of models when applied to data of new specimens. Consequently, we argue that for the most common goals of animal behaviour recognition overall accuracy should be the preferred metric. Considering, that often the goal is to collect movement data without other methods of observation, we argue that training data and testing data should be divided by individual and not randomly.