Gearóid Ó Laighin

Fall detection - Principles and Methods

Fall detection of the elderly is a major public health problem. Thus it has generated a wide range of applied research and prompted the development of telemonitoring systems to enable the early diagnosis of fall conditions. This article is a survey of systems, algorithms and sensors, for the automatic early detection of the fall of elderly persons. It points out the difficulty to compare the performances of the different systems due to the lack of a common framework. It then proposes a procedure for this evaluation.

CAALYX: a new generation of location-based services in healthcare

Recent advances in mobile positioning systems and telecommunications are providing the technology needed for the development of location-aware tele-care applications. This paper introduces CAALYX – Complete Ambient Assisted Living Experiment, an EU-funded project that aims at increasing older peoples autonomy and self-confidence by developing a wearable light device capable of measuring specific vital signs of the elderly, detecting falls and location, and communicating automatically in real-time with his/her care provider in case of an emergency, wherever the older person happens to be, at home or outside.

Home detection of freezing of gait using support vector machines through a single waist-worn triaxial accelerometer

Among Parkinson’s disease (PD) symptoms, freezing of gait (FoG) is one of the most debilitating. To assess FoG, current clinical practice mostly employs repeated evaluations over weeks and months based on questionnaires, which may not accurately map the severity of this symptom. The use of a non-invasive system to monitor the activities of daily living (ADL) and the PD symptoms experienced by patients throughout the day could provide a more accurate and objective evaluation of FoG in order to better understand the evolution of the disease and allow for a more informed decision-making process in making adjustments to the patient’s treatment plan. This paper presents a new algorithm to detect FoG with a machine learning approach based on Support Vector Machines (SVM) and a single tri-axial accelerometer worn at the waist. The method is evaluated through the acceleration signals in an outpatient setting gathered from 21 PD patients at their home and evaluated under two different conditions: first, a generic model is tested by using a leave-one-out approach and, second, a personalised model that also uses part of the dataset from each patient. Results show a significant improvement in the accuracy of the personalised model compared to the generic model, showing enhancement in the specificity and sensitivity geometric mean (GM) of 7.2%. Furthermore, the SVM approach adopted has been compared to the most comprehensive FoG detection method currently in use (referred to as MBFA in this paper). Results of our novel generic method provide an enhancement of 11.2% in the GM compared to the MBFA generic model and, in the case of the personalised model, a 10% of improvement with respect to the MBFA personalised model. Thus, our results show that a machine learning approach can be used to monitor FoG during the daily life of PD patients and, furthermore, personalised models for FoG detection can be used to improve monitoring accuracy.

Upper extremity rehabilitation of children with cerebral palsy using accelerometer feedback on a multitouch display

Cerebral palsy is a non-progressive neurological disorder caused by disturbances to the developing brain. Physical and occupational therapy, if started at a young age, can help minimizing complications such as joint contractures, and can improve limb range of motion and coordination. While current forms of therapy for children with cerebral palsy are effective in minimizing symptoms, many children find them boring or repetitive. We have designed a system for use in upper-extremity rehabilitation sessions, making use of a multitouch display. The system allows children to be engaged in interactive gaming scenarios, while intensively performing desired exercises. It supports games which require completion of specific stretching or coordination exercises using one or both hands, as well as games which use physical, or “tangible” input mechanisms. To encourage correct posture during therapeutic exercises, we use a wireless kinematic sensor, worn on the patients trunk, as a feedback channel for the games. The system went through several phases of design, incorporating input from observations of therapy and clinical sessions, as well as feedback from medical professionals. This paper describes the hardware platform, presents the design objectives derived from our iterative design phases and meetings with clinical personnel, discusses our current game designs and identifies areas of future work.

Dopaminergic-induced dyskinesia assessment based on a single belt-worn accelerometer

BACKGROUND After several years of treatment, patients with Parkinsons disease (PD) tend to have, as a side effect of the medication, dyskinesias. Close monitoring may benefit patients by enabling doctors to tailor a personalised medication regimen. Moreover, dyskinesia monitoring can help neurologists make more informed decisions in patients care. OBJECTIVE To design and validate an algorithm able to be embedded into a system that PD patients could wear during their activities of daily living with the purpose of registering the occurrence of dyskinesia in real conditions. MATERIALS AND METHODS Data from an accelerometer positioned in the waist are collected at the patients home and are annotated by experienced clinicians. Data collection is divided into two parts: a main database gathered from 92 patients used to partially train and to evaluate the algorithms based on a leave-one-out approach and, on the other hand, a second database from 10 patients which have been used to also train a part of the detection algorithm. RESULTS Results show that, depending on the severity and location of dyskinesia, specificities and sensitivities higher than 90% are achieved using a leave-one-out methodology. Although mild dyskinesias presented on the limbs are detected with 95% specificity and 39% sensitivity, the most important types of dyskinesia (any strong dyskinesia and trunk mild dyskinesia) are assessed with 95% specificity and 93% sensitivity. CONCLUSION The presented algorithmic method and wearable device have been successfully validated in monitoring the occurrence of strong dyskinesias and mild trunk dyskinesias during activities of daily living.

An integrated fall and mobility sensor and wireless health signs monitoring system

In this paper the integration of a fall and mobility sensor with a health signs monitoring system is described. In a previous version of the system, which was used in trials with elderly volunteers in Ancona, Italy, health signs were monitored with stand-alone commercially available sensors. Additionally, mobility of the elderly was monitored using a custom-built fall and mobility sensor. After successful completion of these trials, several health signs sensors, including the aforementioned fall and mobility sensor, are now integrated in one device. The integration will solve several issues identified in the previous trials. User acceptance, for example, relies heavily on the number of devices used whereas medical significance of the sensor system hinges on the ability to measure a significant number of parameters. Furthermore a significant advantage can be obtained by validating measurements using mobility data obtained with the fall and mobility sensor. The thus obtained context awareness significantly increases reliability of the health signs automatically gathered by the sensor suite. This paper describes the development of the integrated system.

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.

Analysis of Correlation between an Accelerometer-Based Algorithm for Detecting Parkinsonian Gait and UPDRS Subscales

Background Our group earlier developed a small monitoring device, which uses accelerometer measurements to accurately detect motor fluctuations in patients with Parkinson’s (On and Off state) based on an algorithm that characterizes gait through the frequency content of strides. To further validate the algorithm, we studied the correlation of its outputs with the motor section of the Unified Parkinson’s Disease Rating Scale part-III (UPDRS-III). Method Seventy-five patients suffering from Parkinson’s disease were asked to walk both in the Off and the On state while wearing the inertial sensor on the waist. Additionally, all patients were administered the motor section of the UPDRS in both motor phases. Tests were conducted at the patient’s home. Convergence between the algorithm and the scale was evaluated by using the Spearman’s correlation coefficient. Results Correlation with the UPDRS-III was moderate (rho −0.56; p < 0.001). Correlation between the algorithm outputs and the gait item in the UPDRS-III was good (rho −0.73; p < 0.001). The factorial analysis of the UPDRS-III has repeatedly shown that several of its items can be clustered under the so-called Factor 1: “axial function, balance, and gait.” The correlation between the algorithm outputs and this factor of the UPDRS-III was −0.67 (p < 0.01). Conclusion The correlation achieved by the algorithm with the UPDRS-III scale suggests that this algorithm might be a useful tool for monitoring patients with Parkinson’s disease and motor fluctuations.

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.

When a step is not a step! Specificity analysis of five physical activity monitors

Introduction Physical activity is an essential aspect of a healthy lifestyle for both physical and mental health states. As step count is one of the most utilized measures for quantifying physical activity it is important that activity-monitoring devices be both sensitive and specific in recording actual steps taken and disregard non-stepping body movements. The objective of this study was to assess the specificity of five activity monitors during a variety of prescribed non-stepping activities. Methods Participants wore five activity monitors simultaneously for a variety of prescribed activities including deskwork, taking an elevator, taking a bus journey, automobile driving, washing and drying dishes; functional reaching task; indoor cycling; outdoor cycling; and indoor rowing. Each task was carried out for either a specific duration of time or over a specific distance. Activity monitors tested were the ActivPAL micro™, NL-2000™ pedometer, Withings Smart Activity Monitor Tracker (Pulse O2)™, Fitbit One™ and Jawbone UP™. Participants were video-recorded while carrying out the prescribed activities and the false positive step count registered on each activity monitor was obtained and compared to the video. Results All activity monitors registered a significant number of false positive steps per minute during one or more of the prescribed activities. The Withings™ activity performed best, registering a significant number of false positive steps per minute during the outdoor cycling activity only (P = 0.025). The Jawbone™ registered a significant number of false positive steps during the functional reaching task and while washing and drying dishes, which involved arm and hand movement (P < 0.01 for both). The ActivPAL™ registered a significant number of false positive steps during the cycling exercises (P < 0.001 for both). Conclusion As a number of false positive steps were registered on the activity monitors during the non-stepping activities, the authors conclude that non-stepping physical activities can result in the false detection of steps. This can negatively affect the quantification of physical activity with regard to step count as an output. The Withings™ activity monitor performed best with regard to specificity during the activities of daily living tested.