Measuring Generalization of Visuomotor Perturbations in Wrist Movements Using Mobile Phones

Recent studies in motor control have shown that visuomotor rotations for reaching have narrow generalization functions: what we learn during movements in one direction only affects subsequent movements into close directions. Here we wanted to measure the generalization functions for wrist movement. To do so we had 7 subjects performing an experiment holding a mobile phone in their dominant hand. The mobile phone's built in acceleration sensor provided a convenient way to measure wrist movements and to run the behavioral protocol. Subjects moved a cursor on the screen by tilting the phone. Movements on the screen toward the training target were rotated and we then measured how learning of the rotation in the training direction affected subsequent movements in other directions. We find that generalization is local and similar to generalization patterns of visuomotor rotation for reaching

Fall Classification by Machine Learning Using Mobile Phones

Fall prevention is a critical component of health care; falls are a common source of injury in the elderly and are associated with significant levels of mortality and morbidity. Automatically detecting falls can allow rapid response to potential emergencies; in addition, knowing the cause or manner of a fall can be beneficial for prevention studies or a more tailored emergency response. The purpose of this study is to demonstrate techniques to not only reliably detect a fall but also to automatically classify the type. We asked 15 subjects to simulate four different types of falls–left and right lateral, forward trips, and backward slips–while wearing mobile phones and previously validated, dedicated accelerometers. Nine subjects also wore the devices for ten days, to provide data for comparison with the simulated falls. We applied five machine learning classifiers to a large time-series feature set to detect falls. Support vector machines and regularized logistic regression were able to identify a fall with 98% accuracy and classify the type of fall with 99% accuracy. This work demonstrates how current machine learning approaches can simplify data collection for prevention in fall-related research as well as improve rapid response to potential injuries due to falls

Developmental roadmap for antimicrobial susceptibility testing systems

Antimicrobial susceptibility testing (AST) technologies help to accelerate the initiation of targeted antimicrobial therapy for patients with infections and could potentially extend the lifespan of current narrow-spectrum antimicrobials. Although conceptually new and rapid AST technologies have been described, including new phenotyping methods, digital imaging and genomic approaches, there is no single major, or broadly accepted, technological breakthrough that leads the field of rapid AST platform development. This might be owing to several barriers that prevent the timely development and implementation of novel and rapid AST platforms in health-care settings. In this Consensus Statement, we explore such barriers, which include the utility of new methods, the complex process of validating new technology against reference methods beyond the proof-of-concept phase, the legal and regulatory landscapes, costs, the uptake of new tools, reagent stability, optimization of target product profiles, difficulties conducting clinical trials and issues relating to quality and quality control, and present possible solutions

Axiomatic information measures depending only on a probability measure

This paper characterizes information measures, in the sense of the Axiomatic Information theory introduced by Forte and Kampé de Fériet (1969) which depend only on a probability measure and they are compatible with the most general form of the "independence axiom".General information General independence

Comparative Evaluation of Feature Extraction Methods for Human Motion Detection

Part 2: MHDW WorkshopInternational audienceIn this article we conduct an evaluation of feature extraction methods for the problem of human motion detection based on 3-dimensional inertial sensor data. For the purpose of this study, different preprocessing methods are used, and statistical as well as physical features are extracted from the motion signals. At each step, state-of-the-art methods are applied, and the produced results are finally compared in order to evaluate the importance of the applied feature extraction and preprocessing combinations, for the human activity recognition task

Recognizing whether sensors are on the same body

Abstract. As personal health sensors become ubiquitous, we also expect them to become interoperable. That is, instead of closed, end-to-end personal health sensing systems, we envision standardized sensors wirelessly communicating their data to a device many people already carry today, the cellphone. In an open personal health sensing system, users will be able to seamlessly pair off-the-shelf sensors with their cellphone and expect the system to just work. However, this ubiquity of sensors creates the potential for users to accidentally wear sensors that are not necessarily paired with their own cellphone. A husband, for example, might mistakenly wear a heart-rate sensor that is actually paired with his wife’s cellphone. As long as the heart-rate sensor is within communication range, the wife’s cellphone will be receiving heart-rate data about her husband, data that is incorrectly entered into her own health record. We provide a method to probabilistically detect this situation. Because accelerometers are relatively cheap and require little power, we imagine that the cellphone and each sensor will have a companion accelerometer embedded with the sensor itself. We extract standard features from these companion accelerometers, and use a pair-wise statistic – coherence, a measurement of how well two signals are related in the frequency domain – to determine how well features correlate for different locations on the body. We then use these feature coherences to train a classifier to recognize whether a pair of sensors – or a sensor and a cellphone – are on the same body. We evaluate our method over a dataset of several individuals walking around with sensors in various positions on their body and experimentally show that our method is capable of achieving an accuracies over 80%.

Enhancing accelerometer-based activity recognition with capacitive proximity sensing

Activity recognition with a wearable accelerometer is a common investigated research topic and enables the detection of basic activities like sitting, walking or standing. Recent work in this area adds different sensing modalities to the inertial data to collect more information of the user's environment to boost activity recognition for more challenging activities. This work presents a sensor prototype consisting of an accelerometer and a capacitive proximity sensor that senses the user's activities based on the combined sensor values. We show that our proposed approach of combining both modalities significantly improves the recognition rate for detecting activities of daily living

Activity Recognition System Using Non-intrusive Devices through a Complementary Technique Based on Discrete Methods

This paper aims to develop a cheap, comfortable and, spe cially, efficient system which controls the physical activity carried out by the user. For this purpose an extended approach to physical activ ity recognition is presented, based on the use of discrete variables which employ data from accelerometer sensors. To this end, an innovative se lection, discretization and classification technique to make the recog nition process in an efficient way and at low energy cost, is presented in this work based on Ameva discretization. Entire process is executed on the smartphone and on a wireless health monitoring system is used when the smartphone is not used taking into account the system energy consumptionMinisterio de Economía y Competitividad TIN2009-14378-C02-01 (ARTEMISA)Junta de Andalucía TIC-8052 (Simon