Annals of Biomedical Engineering, Vol. 34, No. 4, April 2006 ( C 2006) pp. 547–563 DOI: 10.1007/s10439-005-9068-2 A Review of Approaches to Mobility Telemonitoring of the Elderly in Their Living Environment CLIODHNA N ´ I SCANAILL, 1 SHEILA CAREW, 2 PIERRE BARRALON, 3 NORBERT NOURY, 3 DECLAN LYONS, 2 and GERARD M. LYONS 1 1 Biomedical Electronics Laboratory, Department of Electronic and Computer Engineering, University of Limerick, National Technological Park, Limerick, Ireland; 2 Clinical Age Assessment Unit, Mid Western Regional Hospital, Limerick, Ireland; and 3 Laboratoire TIMC-IMAG, Facult ´ edeM ´ edecine, 38706, La Tronche Cedex, France (Received 10 May 2005; accepted 8 December 2005; published online: 21 March 2006) Abstract—Rapid technological advances have prompted the de- velopment of a wide range of telemonitoring systems to enable the prevention, early diagnosis and management, of chronic con- ditions. Remote monitoring can reduce the amount of recurring admissions to hospital, facilitate more efficient clinical visits with objective results, and may reduce the length of a hospital stay for individuals who are living at home. Telemonitoring can also be applied on a long-term basis to elderly persons to detect gradual deterioration in their health status, which may imply a reduction in their ability to live independently. Mobility is a good indicator of health status and thus by monitoring mobility, clinicians may assess the health status of elderly persons. This article reviews the architecture of health smart home, wearable, and combina- tion systems for the remote monitoring of the mobility of elderly persons as a mechanism of assessing the health status of elderly persons while in their own living environment. Keywords—Activity, Remote, Review, Health smart home, Wearable, Telemedicine. ABBREVIATIONS ANN Artificial Neural Network BP Blood Pressure BUS Binary Unit System CAN Controller Area Network ECG Electrocardiogram GPRS General Packet Radio Service GSM Global System for Mobile communications IR Infrared PIR Passive InfraRed ISDN Integrated Services Digital Network LAN Local Area Network PDA Personal Digital Assistant POTS Plain Old Telephone System PSTN Public Switched Telephone Network Address correspondence to Cliodhna N ´ ı Scanaill, Biomedical Elec- tronics Laboratory, Department of Electronic and Computer Engineering, University of Limerick, National Technological Park, Limerick, Ireland. Electronic mail: Cliodhna.NiScanaill@ul.ie RF Radio Frequency SMS Short Message Service WLAN Wireless Local Area Network WPAN Wireless Personal Area Network INTRODUCTION The western world is experiencing a so-called “greying population” (Fig. 1). 49 In 2001, 17% of the European Union (EU) was over 65 and it is estimated that by the year 2035 this figure will have reached 33%. This demographic trend is already posing many social and economic problems as the care ratio (the ratio of the number of persons aged between 16 and 65 to those aged 65 and over) is in decline. This trend suggests that there will be less people to care for elderly, as well as a decreased ratio of tax paying workers (who fund the health services) to elderly people (using the health services). Thisproblem is compoundedfurther by the fact that elderly place proportionally greater demands on health services than any other population grouping, outside of newborn babies (Fig. 2). 49 Healthcare delivery meth- ods will need to be adapted to meet the challenges posed by this aging population and to care for this group while constrained by limited resources, but maintaining the same high standards. It is generally expected that the use of tech- nology will be required to create an efficient healthcare delivery system. 9 One such technology, telemonitoring, can be used to monitor elderly and chronically ill patients in their own community, which has been shown to be their preferred set- ting. 29 Telemonitoring can lead to a significant reduction in healthcare costs by avoiding unnecessary hospitalization, and ensuring that those who need urgent care receive it in a more timely fashion. Long-term telemonitoring pro- vides clinically useful trend data that can allow physicians to make informed decisions, to monitor deterioration in chronic conditions, or to assess the response of a patient to a treatment. Telemonitoring has the potential to provide safe, 547 0090-6964/06/0400-0547/0 C  2006 Biomedical Engineering Society 548 N ´ I SCANAILL et al. FIGURE 1. Growth of the UK population as a percentage of the total UK population. (Office of Health Economics, 2006, reproduced with permission.) effective, patient-centered, timely, efficient, and location- independent monitoring; thus, fulfilling the six key aims for improvement of healthcare as proposed by the Institute of Medicine, Washington, DC. 9 Telemonitoring has become increasingly popular in re- cent years due to rapid advances in both sensor and telecom- munication technology. Low-cost, unobtrusive, telemoni- toring systems have been made possible by a reduction in the size and cost of monitoring sensors and record- ing/transmitting hardware. These hardware developments coupled with the many wired (PSTN, LAN, and ISDN) and wireless (RF, WLAN, and GSM) telecommunications op- tions now available, has lead to the development of a variety of telemonitoring applications. Korhonen et al. 19 classified telemonitoring applications into two models—the wellness & disease management model and the independent living & remote monitoring model. Applications covered by the wellness & disease management model are those in which the user actively participates in the measurement and mon- itoring of their condition and the medical personnel play a supporting role. An example of this model is a diabetes management system, in which the user is responsible for measuring and uploading their blood sugar levels to a cen- tral monitoring center. This model is best suited to those who are willing and technologically able to measure their health status and respond to any feedback received. The in- dependent living & remote monitoring model does notplace any such technological demands on the user. In this model, it is the medical personnel who monitors the patient’s con- dition and receives the necessary feedback. Health smart home systems and many wearable systems are examples of this model. The relationship between health status and mobility is well recognized. Increased mobility improves stamina and muscle strength, and can improve psychological well-being and quality of life by increasing the person’s ability to perform a greater range of activities of daily living. 36 Mobility levels are sensitive to changes in health and psychological status. 4 A person’s mobility refers to the amount of time he/she is involved in dynamic activities, such as walking or running, as well as the amount of time spent in the static activities of sitting, standing and lying. Objective mobility data can be used to monitor health, to assess the relevance of certain medical treatments and to determine the quality of life of a patient. The need for expensive residential care (estimated at €100 per patient per day), home visits (estimated at €74 per patient per day), or prolonged stays in hospital (estimated at €820 per patient per day) could be decreased if monitoring techniques, such as home telemedicine (estimated at €30 per patient per day), were employed by the health services. 51 Existing methods for mobility measurement include observation, clinical tests, physiological measurements, diaries and questionnaires, and sensor-based measurements. Diaries and questionnaires require a high level of user compliance and are retrospective and subjective. Observational and clinometric measurements are usually carried out over short periods of time in artificial clinical environments, rely heavily on the administrator’s subjectivity and may be prone to the “white coat” phenomenon. Physiological A Review of Approaches to Mobility Telemonitoring 549 FIGURE 2. Estimated hospital and community health services expenditure by age group, in pound per person, in England 2002/3. (Office of Health Economics, 2006, reproduced with permission.) techniques, though objective, have a high cost per measurement. Long-term, sensor-based measurements taken in a per- son’s natural home environment provide a clearer picture of the person’s mobility than a short period of monitoring in an unnatural clinical setting. By monitoring and recording a patients’ health over long periods, telemonitoring has the potential to allow an elderly person to live independently in their own home, make more efficient use of a carer’s time, and produce objective data on a patient’s status for clinicians. REMOTE MOBILITY MONITORING OF THE ELDERLY Health Smart Homes Smart homes are developed to monitor the interaction between users and their home environment. This is achieved by distributing a number of ambient sensors throughout the subject’s living environment. The data gathered by the smart home sensors can be used to intelligently adapt the environment in the home for its inhabitants 27 or can be studied for the purposes of health monitoring. In Health Smart Homes, 34 the acquired data is used to build a pro- file of the functional health status of the inhabitant. The monitored person’s behavior is then checked for deviations from their “normal” behavior, which can indicate deterio- ration in the patient’s health. Smart home systems passively monitor their occupants all day everyday, thus requiring no action on the part of the user to operate. A large number of parameters can be monitored in a health smart home, by employing a variety of sensors and the processing ca- pabilities of a local PC. Health smart home sensors, placed throughout the house, have fewer restrictions (size, weight, and power) than wearable sensors (which are placed on the person) thus simplifying overall system design. However, health smart homes cannot monitor a subject outside of the home setting, and have difficulties distinguishing between the monitored subject and other people in the home. Health smart homes provide a complete picture of a subject’s health status, by monitoring the subject’s mobil- ity and their interactions with their environment. However, health smart home systems often have little or no access to the subject’s biomechanical parameters, and must therefore measure mobility and/or location indirectly using environ- mental sensors (Table 1). These methods range from simply detecting the subject’s location and recording the time spent there, to measuring the time of travel from one place to another by the subject. Early activity monitoring systems in health smart homes used pressure sensors to identify location. The EMMA (En- vironmental Monitor/Movement Alarm) system, described by Clark 8 in 1979, detected movement using pressure mats (Fig. 3(a)) 50 under the carpets and a vibration detector on the bed. These passive sensors raised an alert unless the 550 N ´ I SCANAILL et al. TABLE 1. Sensors employed in health smart homes. Sensor type Sensor description Pressure sensors 50 An unobtrusive pad placed under a mattress or chair to detect if the bed or chair is in use Pressure mat 26,50 An unobtrusive pad placed under a mat to detect movement Smart tiles 37 Footstep detection tiles, which can identify a subject and the direction in which they are walking Passive infrared sensors 3,4,34,42,54–56 Detects movement by responding at any heat variations. Can be used in broad mode to detect presence in a room or in narrow mode to detect presence in an area. But there is a possibility of false alarms due to heat sources or wind blowing curtains Sound sensors 54 Sensors used to determine activity type Magnetic switches 4,42,54–56 Switches used in doorframes, cupboard and fridges to detect movement or activity type Active infrared sensors 7 Sensors, consisting of an infrared emitter and receptor and placed in a doorway to estimate size and direction through doorway Optical/ultrasonic system 3 Measure gait speed and direction as subject passes through doorway user reset a clock device. Edinburgh District Council 26 also employed both pressure mats and infrared sensors (Fig. 3(b)) 50 to monitor activity in their sheltered housing scheme, thus saving their wardens time and effort. The first telemonitoring health smart home to measure mobility was presented by Celler et al. in 1994. 4 This sys- tem determined a subject’s absence/presence in a room by recording the movements between each room using mag- netic switches placed in the doors, infrared (IR) sensors identified the specific area of the room in which the sub- ject was present, and generic sound sensors detected the activity type. Data from the sensors were collected using power-line communication and automatically transmitted, via the telephone network, to a monitoring and supervisory canter. The British Telecom/Anchor Trust 42,47 health smart home (Fig. 4) 42 also used passive IR sensors and magnetic switches to monitor activity. Radio transmission was used to transfer data between the sensors and the system control box, thus reducing the amount of cabling in the house and FIGURE 3. Smart home sensors (a) pressure mats and (b) pas- sive infrared sensors. (Tunstall Group Ltd., 2006, reproduced with permission.) making the system easier to install and remove. The data were time-stamped and stored on the system control box and then forwarded to the BT Laboratories every 30 min using the PSTN. All data were processed at the BT Labora- tories. If an alarming situation was detected, an automated call was made to the monitored home. The monitored sub- ject could indicate that there was no problem by answering the call and pressing the number “1”. If they pressed the number “2” or didn’t answer the call a nominated contact was notified. This system monitored 11 males and 11 females, aged between 60 and 84, and gathered 5,000 days of lifestyle data during trials. The system generated 60 alert calls, and although according to Sixsmith 47 the majority of alerts raised were false positives, 76% of the subjects thought FIGURE 4. Layout of house monitored by Anchor Trust\BT Lifestyle monitoring system. (Porteus and Brownsell, 2006, re- produced with permission.) A Review of Approaches to Mobility Telemonitoring 551 the sensitivity was just right. Two subjects fell during the trial but both these subjects used their community alarms before the system had sufficient time to recognize the situation. There were several implementation issues in this system. BT had to develop a control box due to the unavailability of a suitable commercial product. It was also necessary to add an additional telephone line to each dwelling solely for the control box. The authors raised the topic of PIR conflicts, noting that it is possible for two or more PIR sensors to be active at the same time. It was also noticed that curtains blowing in the wind caused PIR conflicts. The authors found the development of an algorithm, to distin- guish between an alarming situation and a minor deviation was more difficult than they had originally expected but this distinction became easier to make as more lifestyle data were collected. Perry et al. 40 described a third generation 15 telecare system, The Millennium Home, which has built on the work of the second generation Anchor Trust/BT telecare project. Like it’s predecessor, the Millennium Home was designed to support “a cognitively fit and able-bodied user” and detect any deviations from their normal healthy circa- dian activities using health smart home sensors. However, the Millennium home provides the resident with the op- portunity to communicate with the Millennium Home sys- tem using a variety of home–human (computer-activated telephone, loudspeakers, television/monitor screen) and human–home (telephone, remote-control devicewith a tele- vision/monitor, limited voice recognition) context-sensitive interfaces, which were not available in the Anchor Trust/BT home. These interfaces provide a quick and easy method for the user to cancel false alarms, or to raise an alarm quickly, thus improving on the preceding system. Chan et al. 7 developed a system, which not only detected a subject’s absence/presence in a particular room, but also measured their mobility in kilometers. Active IR detectors and magnetic switches were placed in each doorframe to determine the subject’s direction through the doors and to estimate their size for identification purposes. Passive IR sensors mounted on the ceiling formed circles of diameter 2.2 m on the floor and detected any heat variations caused by human movement within and between these circles. A binary unit system (BUS) linked the sensors and the local PC. An artificial neural network (ANN) monitored the sub- ject’s mobility data for deviations from their usual pattern. This system was based on the assumptions that the moni- tored subject lived alone and had repetitive and identifiable habits. Chan et al. also used this approach in a later system, 6 where IR movement detectors measured the night activities of elderly subjects suffering from Alzheimer’s disease. This system was tested for short term (16 subjects monitored for an average of 4 nights) and long term durations (1 subject monitored for 13 consecutive nights) and good agreement was found between the system and observations made by the nursing staff. However, the authors had difficulties with the IR sensors and noted that they could not detect fast movement or more than a single person in the room. The imprecise boundaries of the IR sensors was also an issue in this system, as the possibility of two or more sensors being active at the same time made the timing of certain events, such as going to bed, difficult. Cameron et al. 3 designed a health smart home that mea- sured mobility and gait speed along with other parameters, to determine the risk of falling in elderly patients. PIR sen- sors were also used in this system to quantify motion within each room. The authors developed an optical/ultrasonic system to measure gait speed and direction as the sub- ject passed through each doorway. In the next evolution of this system Doughty and Cameron, 14 recognizing the importance of accurate mobility and fall data in fall risk calculation, replaced the ambient fall detection sensors with wearable sensors. Noury et al. 33 designed the Health Integrated health Smart Home Information System (HIS 2 ) (Fig. 5), 34 de- scribed by Virone et al., 54–56 to monitor the activity phases within a patient’s home environment using location sen- sors. Data from magnetic switches and IR sensors placed in doorframes were transmitted via a CAN network to the local PC, where the number of minutes spent in each room per hour was calculated. Measured data were compared to statistically expected data each hour. The CAN network requires only a single telephone cable to transfer data from multiple sensors to the local PC, thus reducing the amount of cabling required for a health smart home. CAN networks have sophisticated error detection and the ability to operate even when a network node is defective. In the absence of a clinical evaluation, a simulator was developed to simu- late 70 days of data and test the ability of the system to store large amounts of data and to manipulate these data to produce results. 55 The HIS 2 health smart home initially communicated with a local server using an Ethernet link. In the next evo- lution of the system a PSTN line was used to transfer data to a remote server. However, this method proved costly as the link was continually running. The HIS 2 health smart home now collects the data locally and emails this data, as an attachment, to the remote server every day. This method is also used to alert the remote server in emergency cases. The Tunstall Group, 50 in the UK, provides commercial health smart home solutions for the remote monitoring of elderly patients by using PIRs, door-, bed-, and chair-usage sensors (Figs. 3(a) and 3(b)), amongothers, to determinethe activity level and type of the monitored subject. A gateway unit, placed in the person’s house, stores information from these sensors and downloads it via a telephone line to a central database and an alert is generated if an alarming trend is detected. The carer can review the patient’s data using the Internet and determine what action, if any, is required. Tunstall also have a facility for the carer to request 552 N ´ I SCANAILL et al. FIGURE 5. The HIS 2 smart home. (Nourg et al.; c  2003 IEEE). a current status report for the client by SMS messaging, in order to provide the carer with peace of mind. Wearable Systems Overview Wearable systems are designed to be worn during nor- mal daily activity to continually measure biomechanical and physiological data regardless of subject location. Wear- able sensors can be integrated into clothing 10,32,38 and jewelry, 1,46 or worn as wearable devices in their own right. 5,22,23,25,30,45 Wearable sensors are attached to the subject they are monitoring and can therefore measure physiological/biomechanical parameters which may not be measurable using ambient sensors. However, the design of wearables is complicated by size, weight, and power consumption requirements. 19 Wearable systems can be classified by their data col- lection methods—data processing, data logging, and data forwarding. Data processing wearable systems include a processing element such as a PDA 10,19 or microcontroller device. Data logging and data forwarding systems are those, which simply acquiredata from the sensors and log thesefor offline analysis or forward these directly to a local analysis station. These systems are best suited to cases where the increased processing power of a PC is required to complete complex analysis. Wearables designed for telemonitoring applications must have the capability to transfer their data, for long- term storage and analysis, to a remote monitoring center. Data can be transmitted directly from the wearable to the monitoring center usingthe GSM network, 30,32 or indirectly via a base station, using POTS or the GSM network, 21,46 A portable GSM modem consumes more energy than a local transmission unit but it allows “anytime anywhere” location independent monitoring of a patient. Indirect methods place a range restriction on the monitored subject, as the subject has to be near the base station for the recorded data to be transmitted to the remote monitoring center via the POTS or GSM network. Wearable Sensors Wearable sensors have the ability to measure mobility directly. Pedometers, foot-switches and heart rate measure- ments (calculated by R-R interval counters) can measure a person’s level of dynamic activity and energy expenditure however they do not provide information on the person’s static activities. Accelerometer and gyroscope-based wear- ables can be used to distinguish between individual static postures and dynamic activity. Magnetometers have also been used in combination with accelerometers to assess the giratory movements. 31 Accelerometry is low-cost, flexible, and accurate method for the analysis of posture and movement, 24 with applica- tions in fall detection, gait analysis, and monitoring of a variety of pathological conditions, such as COPD (Chronic Obstructive Pulmonary Disease). 5,25 Accelerometer-based systems have been shown to accurately measure both A Review of Approaches to Mobility Telemonitoring 553 dynamic and static activities in both long 11,22 and short- term situations. 30 Accelerometers operate by measuring acceleration along each axis of the device and can therefore detect static postures by measuring the acceleration due to gravity, and detect motion by measuring the corresponding dynamic acceleration. Gyroscopes measure the Coriolis ac- celeration from rotational angular velocity. They can there- fore measure transitions between postures and are often used to compliment accelerometers in mobility monitoring systems. 28,45 Forthis reason most mobility, gait, and posture wearable applications are accelerometer and/or gyroscope based. However, there is little consensus as to the optimal placement and amount of sensors required to obtain suffi- cient results; with some authors preferring a single sensor unit worn at the waist, 12,22,23,25,59 sacrum 43 or chest 28,31 to multiple sensors distributed on the body. 11,20,30,53 Data Logging Wearables Data logging systems have the advantage of being able to monitor the subject regardless of their location. The dis- advantage of data logging systems is that the subject’s mo- bility patterns cannot be analyzed between uploads. If an alarming trend occurs between uploads it will not be dis- covered until that data is uploaded and analyzed on the pc. This problem will become more significant as improving memory technology increases the time between uploads. Non-telemonitoring data logging systems, 11,20,53 typically used in a clinical setting, require a skilled user to upload the data and perform complex offline analysis. Telemon- itoring data logging systems, 2,32,57 used by elderly sub- jects in their own homes, include simplified data upload mechanisms and automated data analysis and transmis- sion to increase their suitability for non-technically-minded users. The BodyMedia SenseWear (Fig. 6) 2 is such a telemon- itoring data logging system. It is worn on the upper arm and is capable of storing up to 14 days of continuous data from its dual-axis accelerometer, galvanic skin response sensor and heat sensors. The SenseWear can form a Body Area Network (BAN) with other commercial physiological monitors, such as heart rate monitors, to supplement its analysis. The data can be uploaded to the local PC using a USB cable or can be uploaded wirelessly using the wireless communicator module. The associated desktop application, InnerView, retrieves lifestyle data, including energy expen- diture, physical activity, and number of steps, from the SenseWear unit. Data from the SenseWear unit can trans- mitted, via an Internet server, to a health or fitness expert for remote monitoring of the subject’s health status. A carer can be notified by SMS message if an alarming trend has been detected. The SenseWear unit can also operate as a data forwarding device, which wirelessly streams data to the local PC for immediate analysis. FIGURE 6. SenseWear armband. (BodyMedia Inc., 2005, pre- produced with permission). Wearable systems integrated into clothing, such as the VTAMN project 32 and the VivoMetrics Lifeshirt R 10,57 products, can be worn discreetly under clothing. The pro- cess of donning and doffing multiple sensors is simpli- fied by integrating these sensors into clothing. Clothing- based wearables also ensure correct sensor placement. The Lifeshirt 10 is a lightweight, comfortable, washable shirt containing numerous embedded sensors. It measures over 30 cardiopulmonary parameters, and it’s 3-axis accelerom- eter records the subject’s posture and activity level. The sensors are attached, using secure connectors, to PDA device. The data is saved to a flash memory card and can be analyzed locally using VivoLogic software or up- loaded via the Internet and processed by staff at the Data Center who will generate a summary report for the subject. The VTAMN smart cloth (Fig. 7) 32 measures several parameters of daily living, including activity, using sen- sors incorporated into the garment. The activity-measuring module of the VTAMN project is based on a 3-axis ac- celerometer, worn under the subject’s armpit. The data from this module is processed by embedded software and can distinguish between activity, a fall, and standing, lying, and bending postures. The VTAM shirt can connect to a remote call center using the GSM network if it detects an alarm- ing situation. Data can also be transmitted, via the GSM network, from the activity-measuring module to a remote PC, where it is analyzed using further mobility-detection algorithms. 554 N ´ I SCANAILL et al. FIGURE 7. The VTAMN shirt, an example of a wearable system integrated into clothing. (Noury et al. , c  2004 IEEE). Data Forwarding Wearables Data forwarding systems 5,12,22,23,25,46,59 are used when the weight of the wearable system is a key factor, as a data storage or a data processing unit can be replaced by a minia- ture transmitter.Howeverdata forwarding wearables, which typically use RF, Bluetooth, or WLAN, are range-limited, and therefore the data from the subject is not recorded when the subject is outside the range of the receiver. This makes data forwarding systems suitable for housebound subjects but not necessarily those who are independent and have the ability to move outside of the house. Simple accelerometer-based activity monitors, known as actigraphs, can be worn at the wrist, 46 waist, or foot to monitor mobility and are usually a single-axis devices that simply distinguish between activity and inactivity in order to estimate energy expenditure, sleep patterns, and circadian rhythm. While actigraphs were originally local data logging systems that required manual uploading ofdata to a PC, an evolution of these devices are data forwarding systems such as the Vivago device described by Sarela, 46 which can generate an alarm in emergency cases. The Vivago R  device (Fig. 8), 18 described by Sarela et al. 46 in 2003, is a wrist-worn device with a manual alarm button and inbuilt movement measurement, capa- ble of distinguishing between activity and inactivity. The Vivago system continually monitors the user’s activity pat- terns in their home by forwarding data from the wrist unit to the base station. The base station generates an automated alarm if an alarming period of inactivity is detected. The base station is typically connected to the server using the PSTN, or using a GSM modem if the PSTN is not available. The gateway server then transmits the alert, as voice or text FIGURE 8. IST Vivago wrist unit. (IST OY, 2006, 19 reproduced with permission). messages, to the appropriate care personnel. Activity data can be remotely monitored using specially designed soft- ware. This system was evaluated, over three months, on 83 elderly people living at home or in assisted living facilities. Subjects were actively encouraged to wear the device and skin conductivity data, measured by the wrist units, showed that the subjects were within monitoring range (20–30 m) of the base unit for 94% of the time and user compliance was high. Mathie et al., 22,23,25 Wilson et al., 12,59 and Prado et al. 43,44 have each designed more complex systems, capa- ble of measuring both activity and posture, using a single bi- axial or tri-axial accelerometer-unit located at the person’s center of gravity (i.e. waist or sacrum). Mathie et al. 25 used a single, waist mounted, tri-axial accelerometer to mea- sure mobility, energy expenditure, gait and fall incidence in patients with CHF (Congestive Heart Failure) and COPD (Chronic Obstructive Pulmonary Disease). The device was initially placed at the sacrum, but during testing, subjects complained of difficulty attaching the device and discom- fort when sitting with the device attached. It was decided to place the device on the hipbone to improve comfort. How- ever, the authors noted that this placement was more likely to be affected by artifact than placement at the sacrum, and that some distortion of the output signal occurred as the device was not aligned symmetrically (left-right) on the pa- tient. Data were sampled at 40 Hz and forwarded over a RF link to a PC. All parameters in the system were calculated twice a minute, and summarized information was uploaded to a central server each night. Like all data forwarding sys- tems, this system was unable to monitor the subject when they were outside of the range of the RF link. This system implemented telemonitoring by uploading data to a central server every night. At the same conference, Celler et al. 5 described the “Home Telecare System” which combined Mathie’s 25 wearable system, with a fixed workstation (for ECG, BP and temperature measurements) and ambient sen- sors (light, temperature, humidity). Data from the wearable element was collected by a local PC, compressed and trans- mitted during the night to a remote server. Measurements A Review of Approaches to Mobility Telemonitoring 555 taken using the fixed workstation were transmitted to the central server immediately following collection. Passwords were used to control the level of access each user had to the patient’s data on the server. A web interface to the server was provided for the clinicians to observe the patients mo- bility trends. Easy access to the server was necessary for clinicians to monitor mobility trends because automated trend detection and automated summary reports were not implemented in this system. A pilot study of this system 22 was carried out with six subjects, aged between 80 and 86, over a period of 13 weeks. The wearable system was housed in a case (71 mm × 50 mm × 18 mm), which could be clipped to a belt. Healthy subjects, who were likely to still be in their own homes at the end of trial, were selected for this study; consequently, the health status of the subjects remained unchanged throughout the study. A high rate of compliance (88%) was measured, which was attributed by the authors to the simplicity of the system, its unobtrusiveness (subjects forgot they were wearing it), and the computer-generated reminders to wear the system. The high rate of compliance and positive user feedback suggest that the system is suitable for long-term continuous use. The CSIRO “Hospital without Walls” project described by Wilson et al. 59 and Dadd et al., 12 monitors vital signs from patients in their homes using a wearable ultra low- power radio system and a base station located in the home. The wearable module contains a tri-axial accelerometer, and a rubber electrode system for detecting heartbeats, in- terfaced to an RF data acquisition unit. Sensor data can be continuously forwarded from the wearable to the base unit for two days before recharging the batteries on the wearable unit. Processing and storage occur predominantly in the base station PC. Trend and summary data is generated by database software resident on the base station PC. The PC uploads data to a central recording facility every day or in response to an emergency. This data can be accessed remotely by authorized medical staff using a web browser. Data Processing Wearables Data processing wearables consume more power than other types of wearable systems but they can provide real- time feedback to a user and do not require large amounts of data storage, as the raw data are typically summarized in real-time before storage or transmission. The use of sum- marized data also reduces costs by lowering the upload time to the server. CSIRO have developed a data processing mobility mon- itoring system, PERSiMON 41 (Fig. 9), 41 which measures heart rate, respiration rate, movement and activity. The non- contact PERSiMON unit is held in the pocket of an under- garment vest. The 3 accelerometers in the unit are analyzed to measure movement, long-term activity trends and to de- tect falls. Sensor data are processed in the wearable unit in order to produce summaries, and to detect and record FIGURE 9. CSIRO PERSiMON unit. (CSIRO, 2006, reproduced with permission). details of an event. A voice channel is activated in the case of an alarm to reduce the incidence of false positives. The data is transmitted by Bluetooth, to a base station in the home, from where it is uploaded to a remote monitoring center. If the subject carries a Bluetooth and GPRS enabled mobile phone they will be monitored, regardless of their location, provided GSM coverage is available. Veltink et al. 53 demonstrated a dual sensor configuration, where uni-axial accelerometers are placed on the trunk and thigh to measure mobility. Veltink’s configuration has been has been adapted by Culhane et al. 11,20 and validated in a long-term clinical trial of elderly people. This configura- tion was found to have a detection accuracy of 96%, when compared to the observed data. N ´ ı Scanaill et al. 30 adopted this accelerometer configuration, which requires only two data channels to distinguish between different postures and dynamic activities, for a wearable telemonitoring system (Fig. 10). A wearable data acquisition unit processed the data from the chest and thigh accelerometers every second to determine the subject’s posture. A SMS (Short Message Service) message, summarizing the subject’s posture for the previous hour, is sent from the data acquisition unit every hour to a remote monitoring and analysis server. This sys- tem was tested in short-term conditions on healthy subjects and showed an average detection accuracy of over 99%. Prado et al. 43,44 developed a WPAN-based (Wireless Personal Area Network) system that is capable of moni- toring posture and movement of the subject 24 h a day, inside and outside of the home. This system utilizes an intelligent accelerometer unit (IAU), capable of 2 months of autonomous use and which is fixed to the skin at the height of the sacrum using an impermeable patch. The IAU (diameter 50 mm, thickness 5 mm) consists of two dual- axis accelerometers, a PIC microcontroller and a 3 V Li-Ion supply. It can reset itself and inform the WPAN server when 556 N ´ I SCANAILL et al. FIGURE 10. Remote mobility monitoring using the GSM network. it detects hardware failure. The WPAN server includes an alarm button, a display to show the state of the IAU, and an optical/acoustic signal to confirm transmission to a remote unit. Low power ISM-band FSK RF transmission was used to communicate within the WPAN and a Bluetooth link was used to transfer data between the WPAN server and the remote access unit (RAU). Several alternatives were explored for the transmission of data from the RAU to the telecare center, 44 including POTS, GSM, ISDN, and X.25 protocol. The X.25 protocol was chosen for cost-efficiency, security reasons, ubiquitous access (especially in rural ar- eas), development time, and ease of use. Combination Wearable/Health Smart Home Systems Health smart home systems developers have recently been integrating wearable sensors into their systems in or- der to make more accurate physiological and biomechanical measurements. These systems combine the physiological and location-independent monitoring advantages of wear- ables with the less severe design constraints of a health smart home. Combination wearable/health smart home sys- tems are those, which used both wearable and health smart home sensors to measure mobility. Systems, such as the Hospital without Walls project, 12,59 which monitors mobil- ity using a wearable, and uses ambient sensors to make non-mobility measurements (such as weight, and blood pressure) are not considered as combination systems for the purposes of this review. Fall detection using only ambient sensors is compli- cated as there is no direct access to the subject who is falling. This makes it difficult to distinguish between a subject falling and a heavy object being dropped. If a fall is properly recognized using the ambient sensors the sys- tem has to decide if it is a recoverable fall or if an alarm must be raised. Doughty and Costa 16 developed a telemon- itoring health smart home with a wearable fall detection element. The wearable element consists of pressure pads in the shoes to count steps, tilt sensors to detect transfers, and shock sensors to detect falls. The health smart home element indirectly monitored location using sound sensors, and switches on the lights and television. The following year Doughty and Cameron 14 incorporated a wearable fall detector into their already developed fall risk health smart home, to improve the accuracy of their fall detection system. The combination wearable/health smart home system de- signed by Noury et al. also used a wearable sensor to detect posture and movement after a fall but used ambient sensors (magnetic switches and IR sensors) to monitor location. Activity monitoring using wearables in a health smart home environment provides more accurate data than mon- itoring with ambient sensors alone. Virone et al. described an ambulatory actimetry sensor in several of the papers describing the HIS 2 health smart home. 13,33,56 The sen- sor continuously detected physical activity, posture, body vibrations and falls. Ambient sensors in the HIS 2 home provided data on the patient’s circadian activity. DISCUSSION Smart Homes Health smart homes, wearables, and combination systems monitor mobility using a variety of sensor and [...]... situation or their ability to raise an alarm may be compromised Overall, the ability to detect a worrying trend and raise an appropriate alarm is very important to elderly people39 A Review of Approaches to Mobility Telemonitoring who fear they will remain unattended in the event of an accident The increasing use of telemonitoring to support independent living inside and outside of the home inevitably... transfer A Review of Approaches to Mobility Telemonitoring Telemonitoring data logging systems allow the person to be monitored regardless of their location and allow complex analysis to be performed off-line using the processing power of a PC The expanding storage capabilities of modern data logging systems suggest that the period between data uploads will increase An excessive period between uploads is to. .. incapable of operating a monitoring system Wearable data forwarding systems, the lightest wearable option, are suited to the frail and housebound as they analyze the data in real-time and can raise immediate alerts Data-logging wearables are suitable for monitoring multi-parameter, long-term trends of healthy elderly subjects, who regularly leave their homes However they are not suited to real-time alarm... health technology are relatively undeveloped and even fewer mechanisms exist to take actions based on the results of such evaluations.52 The decision-making process for selecting a telemonitoring system should be similar to the decision-making process used when selecting a therapy The clinician examines the advantages and disadvantages of employing telemonitoring, and also examines the advantages and... be avoided as a worrying trend which occurs within this period may be missed Rather, the increased data storage capability should be used to improve the quality of data, by increasing the sampling frequency or by monitoring additional relevant parameters Data forwarding systems, such as the Vivago system described by Sarela et al.,46 allow real-time complex analysis of mobility data on a local PC They... PC as an intermediate stage in future mobility monitoring systems PAN- and WPAN-based systems, with the ability to plugand-play new sensors or third-party devices into the existing monitoring system, increase the flexibility of wearable systems and enable easy upgrading and maintenance of the systems Therefore, the advantages that once attracted people to health smart homes (discretion, multi-parameter... operate it (suited to persons with dementia) 1 Direct access to biomechanical parameters 2 Data logging and data processing wearables measure mobility regardless of location 3 Technological advances leading to reduced size, weight and cost of systems 1 Monitoring inside and outside of the home 2 Combines advantages of wearable and health smart home systems transmission in a health smart home Telemonitoring. .. disadvantages of not employing telemonitoring, which is slightly different CONCLUSION Mobility telemonitoring is a growing area, which enables the subjective monitoring of the health status of elderly people living independently in their own homes It provides the clinician with continuous quantitative data that can indicate an improvement or deterioration in a patient’s condition Telemonitoring also... As a result, once the subject is out of range of the base station, the subject’s data are not received by the base station and are therefore not analyzed These wireless technologies include Bluetooth, WLAN and ISM Low-power Bluetooth (0.3 mA in standby mode and 30 mA during sustained data transmissions) has a range of 10 m, making it ideal for Personal Area Networks (PAN) or communicating with a base... for their mobility to be measured (Table 5) Though, it gives the possibility to the person to doff the wearable for a while and still be monitored by the smart home Practical, Functional and Ethical Issues Elderly people wish to remain living in their own homes for as long as possible provided they are safe Technology, and in particular telemedicine, has a role to play in achieving this goal by reassuring . defective. In the absence of a clinical evaluation, a simulator was developed to simu- late 70 days of data and test the ability of the system to store large amounts. accelerometer, integrated into vest Data logging Flash card, manually uploaded to PC Internet transfer A Review of Approaches to Mobility Telemonitoring 559 Telemonitoring