Pepijn van de Ven

A comparative review of wireless sensor network mote technologies

In the past 10 years, wireless sensor networks have grown from a theoretical concept to a burgeoning modern technology. In this paper, we present a comparative review of several wireless sensor network motes. We analyze these WSN devices under a number of different parameters and criteria, including processing ability, expected lifetime and measurement capabilities. We compare and contrast the selected WSN motes under these different headings, highlighting the individual motes performance under each category

Testing of a long-term fall detection system incorporated into a custom vest for the elderly

A fall detection system and algorithm, incorporated into a custom designed garment has been developed. The developed fall detection system uses a tri-axial accelerometer to detect impacts and monitor posture. This sensor is attached to a custom designed vest, designed to be worn by the elderly person under clothing. The fall detection algorithm was developed and incorporates both impact and posture detection capability. The vest and fall algorithm was tested by two teams of 5 elderly subjects who wore the sensor system in turn for 2 week each and were monitored for 8 hours a day.

Neural network control of underwater vehicles

In this paper, recent research efforts in the field of the application of neural networks (NNs) for the control of (semi-)autonomous underwater vehicles are reviewed. Based on a literature review the authors propose a classification of approaches to control underwater vehicles using NNs and the presented articles are categorized according to the identified categories. Based on practical results as described in the discussed literature this paper presents a qualitative assessment regarding the performance of the control strategies. Per category, or control strategy, the major advantages and disadvantages are identified and discussed.

Assessment of waist-worn tri-axial accelerometer based fall-detection algorithms using continuous unsupervised activities

This study aims to evaluate a variety of existing and novel fall detection algorithms, for a waist mounted accelerometer based system. Algorithms were tested against a comprehensive data-set recorded from 10 young healthy subjects performing 240 falls and 120 activities of daily living and 10 elderly healthy subjects performing 240 scripted and 52.4 hours of continuous unscripted normal activities.

Activity recognition with smartphone support

In this paper, the authors describe a method of accurately detecting human activity using a smartphone accelerometer paired with a dedicated chest sensor. The design, implementation, testing and validation of a custom mobility classifier are also presented. Offline analysis was carried out to compare this custom classifier to de-facto machine learning algorithms, including C4.5, CART, SVM, Multi-Layer Perceptrons, and Naïve Bayes. A series of trials were carried out in Ireland, initially involving N=6 individuals to test the feasibility of the system, before a final trial with N=24 subjects took place in the Netherlands. The protocol used and analysis of 1165min of recorded activities from these trials are described in detail in this paper. Analysis of collected data indicate that accelerometers placed in these locations, are capable of recognizing activities including sitting, standing, lying, walking, running and cycling with accuracies as high as 98%.

Chronic neck pain and exercise interventions: frequency, intensity, time, and type principle.

OBJECTIVE To identify the most effective components in an active exercise physiotherapy treatment intervention for chronic neck pain based on the frequency, intensity, time, and type (FITT) exercise method of tailoring physical activity recommendations to the individual needs and goals of patients. DATA SOURCES Databases, including the Allied and Complementary Medicine Database, Cumulative Index to Nursing and Allied Health, MEDLINE, SPORTDiscus, Biomedical Reference Collection, and Academic Search Premier, were searched for relevant articles. STUDY SELECTION Quantitative design studies that included active exercise as part of a multimodal or stand-alone approach were selected. Only studies scoring ≥6 on the Physiotherapy Evidence Database Scale were included in the review because this reflected a good level of evidence. DATA EXTRACTION Study methodologies and relevant outcome measures, including isometric strength, Neck Disability Index scores, and pain scores, were extracted from relevant articles and grouped together for appraisal and synthesis. DATA SYNTHESIS Evidence from selected articles was synthesized according to the FITT exercise principal to determine the most effective exercise type, frequency, and intensity in the treatment of chronic neck pain. CONCLUSIONS Physiotherapy interventions using a multimodal approach appear to produce more beneficial outcomes in terms of increased strength, improved function, and health-related quality of life and reduced pain scores. Active strengthening exercises appear to be beneficial for all of these outcomes; the inclusion of additional stretching and aerobic exercise components appear to enhance the benefits of an exercise intervention.

Innovative ICT solutions to improve treatment outcomes for depression: the ICT4Depression project

Depression is expected to be the disorder with the highest disease burden in high-income countries by the year 2030. ICT4Depression (ICT4D) is a European FP7 project, which aims to contribute to the alleviation of this burden by making use of depression treatment and ICT innovations. In this project we developed an ICT-based system for use in primary care that aims to improve access as well as actual care delivery for depressed adults. Innovative technologies within the ICT4D system include 1) flexible self-help treatments for depression, 2) automatic assessment of the patient using mobile phone and web-based communication 3) wearable biomedical sensor devices for monitoring activities and electrophysiological indicators, 4) computational methods for reasoning about the state of a patient and the risk of relapse (reasoning engine) and 5) a flexible system architecture for monitoring and supporting people using continuous observations and feedback via mobile phone and the web. The general objective of the ICT4D project is to test the feasibility and acceptability of the ICT4D system within a pilot study in the Netherlands and in Sweden during 2012 and 2013.

The design and development of a long-term fall detection system incorporated into a custom vest for the elderly.

A fall detection system and algorithm, incorporated into a custom designed garment has been developed. The developed fall detection system uses a tri-axial accelerometer, microcontroller, battery and Bluetooth module. This sensor is attached to a custom designed vest, designed to be worn by the elderly person under clothing. The fall detection algorithm was developed and incorporates both impact and posture detection capability.

Integration of a suite of sensors in a wireless health sensor platform

In this paper we discuss the development and clinical evaluation of a wireless platform for health signs sensing. The sensors measure physical activity, ECG, blood oxygen saturation, temperature and respiratory rate. An important aspect of the approach is that the sensors are integrated into one waist-worn device. A mobile phone collects data from this device and uses data fusion in the scope of a decision support system to trigger additional measurements, classify health conditions or schedule future observations. In these decisions, the users current physical activity plays an important role as the validity of many health signs measurements is strongly related to physical activity. Due to the integration of the sensors and the use of data fusion it is possible to accurately identify health risks and to react promptly. During clinical trials, for which proper ethical approval was obtained, the system was used by healthy elderly volunteers in Limerick (Ireland) and Ancona (Italy). Results of these trials are also discussed in this paper.

A wearable wireless platform for fall and mobility monitoring

In this paper a new wearable wireless fall and mobility monitoring platform is presented. The platform was developed as part of a European Commission funded project called CAA-LYX. The fall and mobility sensor is based on the use of a tri-axial accelerometer. With the accelerometer, impacts are recorded and together with mobility data, also obtained from the accelerometers, fall events are identified. Fall event data, but also raw accelerometer signals, can be conveyed to a mobile phone or PC using a Bluetooth connection. In laboratory based fall trials with young healthy subjects a PC was used to store all data coming from the fall and mobility sensors. A mobile phone was used in an experiment with elderly people performing normal activities of daily living, where only the user status was conveyed to a server and raw accelerometer data were stored locally. With the phone, the whole system is wearable and can relay alarms to a care taker wherever the user may be. During the experiments, in which a total of 165 simulated ADL, 264 simulated falls and 833 hours of real ADL were collected, the fall and mobility sensor demonstrated its ability to accurately identify fall events.