LIFE SAVINGSOLUTIONS AND SERVICES

FOR YOUR FUTUREKK

EMERGENCY & TELEHEALTH SOLUTIONS

Combining 25 years of industry experience in personal emergency technologies and unmatched professionalism, Life Warning delivers soluons, tailored to customers unique health monitoring needs.

LIFE WARNING, Inc.

Life Warning is a company that specializes in medical monitoring technologies, & offers select soluons and services for seniors & caregivers. Our company's extensive experience and professionalism allow us to deliver soluons that help our customers achieve high levels of freedom and health monitoring anywhere within the United States and abroad*.

WHO WE ARELife Warning is a company that was established in 2010 by experienced and knowledgeable professionals with eextensive backgrounds in the PERS industry and healthcare. Since that me, extensive development work has been undertaken to build a strong team with unmatched experience for delivering quality technology soluons for seniors and caregivers.

WHAT WE BELIEVE IN We believe that a wide range of proven and quality soluons, services, all the features and benefits delivered by them, as well as our aspiaspiraon to connuous improvement of offered soluons, will allow customers to achieve a high level of health monitoring in their homes and away. We believe that our experse allows us to deploy superior soluon to bring customers the assurance of reliability and safety for themselves and there families.

WHAT WE DO WHAT WE WANT TO BELife Warnings objecve is to become the number one medical alert systems provider in the health-care industry. We aim to gain the trust of our clients by delivering the latest advanced technolo-gy and dedicated monitoring soluons from a company comprised of experts in the industry. We desire to be a company that is able to fully sasfy all the monitoring needs of our customers, both families and care providers.

The company monitors the elderly and the sick through advanced monitoring plaorms that allow for personal emergency response in the event of a fall event; and tele health soluons to support medical adherence. The company provides reliable monitoring soluons to cucustomers across a wide range of health con-cerns such as vital signs, blood glucose, blood pressure and acvity monitoring.

* Pending Cerficate of European Conformity (CE)

IN BRIEF

CustomerSoluons

DELIVERING COMPREHENSIVE SOLUTIONS FOR SENIORS AND CAREGIVERS

Monitoring Services

Mobile Soluon

Care Management

Algorithms

Product Design

Rx Management

Vital Sign Monitoring

Online Acvity Monitoring

Product Descirpon

Acvity Sensing

Beyond PERS

Chronic Disease Monitoring

Response Center Services

Fall Detecon

Fall Consequences

Classificaon & Sensors

3D Accelerometer, Gyroscope, Microphone

Tele- Health Soluons

Global Connecvity

Pilot Results

High Tech - No Tech

Acvity Planing

Confidenal

WHYLIFE WARNING?

100%EMT TrainedResponders

5 STARcSAA

CALL CENTER

50STATE LAUNCH Q2 2013

25yTeam Experience

RELIABLE PARTNERWith fully integrated systems and projects already implemented within the United States and near future internaonal roll out, Life Warning is a trusted business partner thanks to a deep understanding of the PERS market and telehealth soluons through world class providers.

Experienced TeamLife Warning is a team of skilled professionals with over 25 years of combined experience in the medical alert and monitoring field. The experience of our specialists and execuves is the foundaon for everything we do. We know exactly how to deliver the best medical soluon and monitoring services to our cucustomers. This extensive experience and market knowledge ensure soluons are up-graded to meet the exact and evolving requirement, and changing needs of todays health care market.

Soluon ProviderLife Warnings, working as system provider, offers reliable and secure soluons when combining soware and hardware compo-nents from different vendors and manufacturers, and integrates them into one working system. Thanks to our extensive eexperience in the PERS industry and system providers, we can customize systems in a proprietary format that meets all operaonal needs and grows accordingly.

Future FocusedWe are a future- focused company that connues to evolve, improving the quality, safety, and producvity of all of our offered soluons in response to cconnuing changing technology. On an ongoing basis the company follows new trends in the PERS market, implemenng innovave and reliable soluons tailored to customers medical needs.

BEYOND

LIFE WARNING PLATFORM

PERS

Life Warning provides seniors and their families freedom, security and the ability to manage their health at home or on the go. Life Warning offers, a whole new level of device, a “Personal Health Gateway” that integrates telehealth with Mobile Emergency Response Service (mPERS) safety features in one small, easy-to-wear mobile device. LiLife Warning’s PERS works virtually anywhere delivering the funconality of a personal health device and more . Easily integrated with always-on connecvity, instant hands-free communica-on, automated fall detecon, locaon tracking, remotely managed two-way voice, and Blue tooth connecvity to home health devices.

Automa cally detects falls Captures health measurementsGPS loca on servicesHands-free, two-way voice Blue tooth pairing w/ devices

CAPABILITY

LIFE WARNING

WHAT WE DO BEST

NETWORK REDUNDANCY

TRAINING & DEVELOPMENTLife Warning leads the way with the industry’s most stringent hiring standards. OurEMT trained personnel meet very strict educaonal rrequirements. They also receive thorough back-ground checks and connuous drug screening. Training is as demanding as it is comprehensive with an intensive six-week program conducted by SIA-cerfied instructors. Monthly meengs to review policies, procedures and technological advances foster connued educaon.

In this day and age, physical security is not enough. Our call center ulizes the highest network security possible to protect all data communicaons. Network security includes dualNetWolves WolfPac firewall servers-impenetrable to outsiders-and redundant Blue Ridgeccrypto servers that create a VPN “cryptographic tunnel” for external remote access.

In the event of a medical emergency our SIA - Cerfied EMT operators will always have an up to date and precise device geo locaon, and will speak immediately through the two way device speaker to provide immediate help. Call center representaves will remain on the line with cli-ents unl help arrives, while connuing to speak with and with and reassure clients that help is on the way. Subsequent team members will inform the paramedics of the clients medical history. In the event of a fall detecon and no response; oper-ators will immediately send for paramedics!

* Pending Cerficate of European Conformity (CE)

RESPONSE CENTER

ACTIVITY SENSING GEO TRACKING/ FENCING

GEO TRACKING/ FENCING Geographic movements are recorded and transmied through Life Warnings mobile device. Client movements and locaon are communicated back to our web based monitoring plaorm. Here, Geo locaon parameters and safe zones can be be easily adapted and applied to individualized user/user groups. Online Geo classificaons allow caregivers to be nofied in the event a client wanders or strays out of there immediate comfort area.

Life Warning’s mobile device connuously monitors the acceleraons and movements of clients. Acvies are recorded and processed through cloud based classificaon systems ddetecng suspicious acvies from falls, near falls and acvies of daily living (ADL). These acvies are then communicated back to our medical monitoring team and online plaorm for immediate noficaon. User orieorientaons and acvity sensing can be easily adapted and individualized for each user/user group.

* Pending Cerficate of European Conformity (CE)

ACTIVITY MONITORING

PRODUCT REVIEW

Life Warning’s device is a complete service that includes a discrete, wearable device to provide highly sophiscated fall detecon capabilies for Personal Emergency Response Services (PERS), GPS-tracking & locaon, and two-way automated hands-free communicaon from virtually any-where via cellular networks. The device connects directly to a monitoring call center for instant assistance whenever and wherever needed.

WIDE AREA CELLULAR CONNECTIVITY3G GSM Quad band 850/900/1800/1900MHZ simultaneous Voice and DataLOCATION SERVICESConfigurable assisted GPSAUDIOFull duplFull duplex speaker phone with 90db +/-2db @ 0.1 metersLOCAL AREA CONNECTIVITYBlue tooth EDR 2.1MANUAL ALERTMechanical touch buon acvang data simultaneous data transmission to call cecenter and establishing voice call.

AUTOMATIC FALL DETECTIONSensor: paired accelerometer, magnetometer, and Gyroscope, 800msec sound recording BATTERY30 minute acve voice call / 36 hour standbyDIMENSION65 X 38 X (12-16) mm65 X 38 X (12-16) mmCASINGSealed poly carbonate thermal plasc casingWEIGHT45gEnvironmentIP56 (shower proof), TTemp -15 to 45C, IK06 (2M drop resistant)

PRODUCT SPECIFICATIONS

PRODUCT SUMMARY

* Pending Cerficate of European Conformity (CE)

CARE MANAGEMENT

Life Warning’s secure, web based monitoring plaorm allows caregivers and geriatric care managers to acvely measure, monitor and manage user health concerns.

Online ManagementChronic Disease MonitoringAcvity SensingRx ManagementGEO Locaon Services

ONLINE MANAGEMENT HIGH TECH SOLUTIONSOur experse allows us to deploy superior soluons bringing customers the assurance of rreliability and safety for themselves and their families. For adult children assisng aging family members, the need to reduce the struggles of daily care giving has never been greater. Life Warning’s interacve source of elder care services is securely accessible from anywhere, aat anyme, with any internet browser, allowing family members to work together with caregivers.

UNDERSTANDING FALLS HEALTH CONSEQUENCES

Fall Consequences

A serious consequence of a fall is a “long – lie”, an extended period of me where the vicm remains immobile on the ground. The “long – lie” occurs in more than 20% of elderly admied to the hospital due to ffalling and can lead to hypothermia, dehydra-on, broncho pneumonia, and pressure sores (Masud & Morris, 2001). Previous works in the field of elderly populaon fall circumstances found that 82% off falls occurred from a standing height, and that a forward fall is the most common falltype (O'Neill, et al., 1994), (Vellas, Wayne, Garry, & Baumgartner, 1998). Those findings may indicate that falls mostly occurred during walking. Sideways (lateral) falls are less common but can cause a lateral force that may fracture an elderly persons hip.

The Internaonal Classificaon of Disease–9 defines a fall as; ‘a fall is an unexpectedevent where a person falls to the ground from an upper level or the same level’.According to the Center of Disease Control and Prevenon (CDC) one out of three adultsaage 65 and older falls each year. Falls are the leading cause of injury related death,non-fatal injuries and trauma hospitalizaon among people 65 and older (Masud &Morris, 2001). Injuries sustained from falls include broken bones, superficial cuts andababrasions to the skin as well as connecve and so ssue damage. Fall related admissions of older adults are a significant financial burden to the health services worldwide. The CDC eesmates that in 2000, direct medical costs of falls totaled a lile over $19 billion—$179 mil-lion for fatal falls and $19 billion for nonfatal fall injuries (Center of Disease Control and Prevenon, 2010).

FALL DETECTION

This literature reviews present Real---Time fall detecon algorithms designed for data input from one wireless wearable sensor node, which may include more than one sensor. A subsequent pre-sentaon will provide a summary of researched fall characteriscs, and the locaon of the node on the subject’s body is discussed.

Exisng fall detecon methods can be divided into two main categories, based on a wearable device or environment based. A wearable device is using moon---tracking sensor like accelerom-eters and gyroscopes, and environmental methods use sensors in the subjects’ surrounding such as video, audio, and vibraon signal. The majority of the academic work on wearable fall detec-on device the research was based on accelerometers. An Australian group (Boyle & Karunanithi, 2008) developed a movement classificaon algorithm based on a bi---axial accelerometer and its change rate and was able to detect forward and backward falls. Chen et al. (Chen, Kwong, Chang, Luk, & BajcLuk, & Bajcsy, 2005) presented a device containing a 3D accelerometer worn on the wrist. The al-gorithm proposed is acceleraon threshold and orientaon based.

When a threshold value is crossed a fall is suspected. Then the change in orientaon derived from the acceleraon vector prior and post to the event. The orientaon is esmated over a one second signal prior and post to the event it is assume that the orientaon is not changing during that me, which may not be the case for dynamic situaons like running or even walking. Wang et al. (Wang, et al., 2008) pleased a 3D accelerometer above the ear. The algorithm ulized the total acceleraon and the sum of frontal and sagial acceleraon component (the horizontal plain). It also took into account the total velocity (calculated using acceleraon integraon). This sese ng was able to detect 100% of the tested events.

Kangas et al. (Kangas, Kon la, Winblad, & Jamsa, 2007) suggested several parameters for thresh-old algorithms. The authors divided the measured acceleraon to dynamic and stac compo-nents. The two components were derived from the 3D acceleraon signal. The stac component was used in posture analyses and the dynamic data in moon analyses and vercal acceleraon calculaon. Bourke et al. (Bourke, O’Donovan, & ÓLaighin, The idenficaon of vercal velocity profiles using an ineral sensor to invesgate pre---impact detecon of falls, 2008) also re-searched the properes of vercal velocity for fall detecon and found it to be a predicve fea-tuture; that was able to detect the fall 323ms prior to impact. Bourke and Kangas compared fall de-tecon algorithms based on features extracted from 3D acceleraon signal. The features targeted different fall characteriscs as impact, velocity, and posture. The algorithms used those character-iscs with increasing complexity, first only the impact was used, then impact and posture and so on. Both authors found simpler algorithms to be more accurate.

FALL DETECTIONALGORITHMS

A fall starts with a short free fall where the acceleraon should decries bellow 1G. When the suject hits the ground the acceleraon increases, above 3G (Sposaro & Tyson, 2009). Immediately aer the impact there is an aer shock, evident in fast fluctuaons of the acceleraon signal. If there is a serious injury the subject will stay on the ground and the acceleraon should be close to 1G. Furthermore body posion should change by approximately 90°, when moving from the up-right to the horizontal posion. Typical fall duraon is approximately 1.5 seconds (Chen, Kwong, Chang, Luk, & Bajcsy, 2005). The below graph shows a typical fall accelerometer reading.

In order to detect human falls an accelerometer, magnetometer, gyroscope, and a micro-phone were used. Data from Falls, Nearly Falls and ADLs were recorded from a single sensor node. The signals were processed and relevant features were extracted from each event. Gaussian mixture model (GMM) classifier was implemented on a subset of a select-ed features space, selected via a SFFS algorithm designed to maximize the classifier perfor-mance. Each class, Fall and ADL (the Nearly falls were labeled as ADLs for the purpose of classificaon), was represented by a GMM trained using an expectaon maimizaon algo-rithm. When a nrithm. When a new event is recorded, the signal undergoes processing, feature extracon and classificaon according to its compability to the previously trained models.

Classificaon System

Fall Charcteriscs

ALGORITHMSFALL DETECTION

Any other works ulize addional sensors, Sposaro and Tyson (Sposaro & Tyson, 2009) and Dai et al. (Dai, Bai, Yang, Shen, & Xuan, 2010) developed fall detecon smart phone applicaon using both its acceleraon and orientaon sensors. There are very few methods proposed in the litera-ture that ulizes the acousc properes of a fall. Most of the related work focuses on collecng and analyzing sound captured from the subject’s environment. Grassi et al. (Grassi, et al., 2008) developed a prototype using three sensors, a 3D camera, a wearable accelerometer, and a staon-ary microphone. Each sensor detecng fall events separately and in the future the results meant tto be fused. The audio signal were used to train an HMM network based on Perceptual Linear Pre-dicon (PLP) features. However the classificaon results were very poor, 60% reliability was achieved. It is very difficult to isolate sounds in close proximity of the subject using microphones.

A Greek group is the only one to use a wearable microphone for fall detecon (Doukas & Maglogi-annis, 2008). The authors suggested a wearable system containing a 3Daccelerometer and a microphone, worn on the foot. Two young volunteers preformed a set of ADL and falls with back-ground noise of falling objects and radio. A short me fourier transform was applied for frequency analysis. A human fall generates low frequency <200Hz, and high---energy sound, those properes allowed falls to be correctly classified in 80% of the mes using audio signal alone. The authors also presented a fusion of the two sensor data using a Support Vector Machine (SVM) classifier.

There are great number of works in the field of fall detecon using mulple sensor nodes in different body locaons. For instance (Li, Stankovic, Hanson, Barth, & Lach,2009) used two accel-erometers and gyroscopes, one on the chest and another on the thigh. This can improve detecon for challenging fall events like vercal falls, however impraccal due to the need to aach several devices to the body.

Another approach is to place sensors in the subjects’ environment; those methods ulize video, audio or vibraon signals recorded from staonary sensors. A GMM based classificaon system susuggested by Zigel et al. (Zigel, Litvak, & Gannot, 2009) used vibraon and audio signals to achieve highly accurate results (97.5% sensivity and 98.6% specificity). Zhuang et al. (Zhuang, Huang, Potamianos, & Hasegawa---Johnson, 2009) presented a high complexity classificaon system based on GMM supervectors. The system used merely on staonary microphone and therefore achieved only 67% detecon rate. Such systems and methods require one or more sensors to be distributed in several locaons on or in the vicinity of the user, which is not convenient for the user nor is it praccal to implement when the user is in an unfamiliar environment. Moreover, the classificlassificaon methods employed hereinabove typically require connuous monitoring and classifi-caon, thereby consuming large amounts of processing and electrical power.

ALGORITHMSFALL DETECTION

Life Warning’s device ulizes one sensor node wearable device containing an accelerometer, mag-netometer, gyroscope and microphone. In order to save power the device connuously samples the acceleraon alone and runs a triggering algorithm to detect suspicious events. If such an event is detected the sensors samples are recorded and transmied to the cloud for addional processing. A classificaon decision is then communicated back to the device. The suggested system uses only one mobile device that can be worn in mulple locaons and orientaons.

In oIn order to detect human falls an accelerometer, magnetometer, gyroscope, and a microphone are used. All data from falls, near falls and ADLs are recorded from a single sensor node. The signals asignals are processed and relevant data are then extracted from each event. Selected feature space, selected via a SFFS algorithm designed to maximize the classifier performance. Each class, Fall and ADL (the near falls are labeled as ADLs for the purpose of classificaon), represented by a GMM trained using an expectaon maximizaon algorithm. When a new event is recorded, the signal undergoes processing, feature extracon and classificaon according to its compability to the previously trained models.

See LifeWarning.com for full summary.

FALL DETECTION

LIFE WARNING

WHO WE ARE

t

TELEHEALTH

Life Warning’s web based plaorm supports medicaon reminders by allowing caregivers and users to program friendly medicaon reminders which are prompted 24/7 for assured medicaon adherence.

External sensor readings of blood oxygen saturaon and pulse rates can be uploaded and sent to care professionals at anyme. Once uploaded and transferred, health care professionals will have the data to make best clinical judgments on how any paent is doing in seconds.

Vital Sign Monitoring

Life Warning’s device works globally in combinaon with external sensors to collect real me data to upload and report user readings. Digitally transmied data can then be automacally transmied to physicians or caregivers.

Chronic Disease Monitoring

Rx Management

Life Warning’s device is a mobile health gateway. It enables users to easily upload biometric measurements from a variety of health devices, as well as receive personalized reminders to take medicaons, upload informaon, and receive coaching specific to the users’ health condions.

Health Gateway

Operaonal excellence, from the first posion with Life Alert, to market reposioning with entrepreneurial start-ups in Medical Device Sales, from "scratch," to AT&T Healthcare Soluons: developing corporate strategic plans and developing product road maps for global markets. Life Warning’s President is a proven leader in strategic and taccal B2C sales and partner development in consumer direct programs in allied business segments and adjacent sales channels. Including products & services addions that are "best in class," sourced worldwide, to enable market leadeleadership with and for partners that are market leaders.

With over 20 year of management experience, Life Warning’s Vice President of operaons is heavily involved in daily business operaons. Experience developed by encompassing a diverse management background. Prior to working in the health and wellness industry Life Warning’s VP of operaons served as Senior VP of one of the largest commercial banking instutes; formed several successful entrepreneurial start-ups; authored numerous books on business management and taught premier business management courses.

Life Warning’s sales director is adept at turning business intersecons into sales channel freeways. Responsible for 3X core business results (post reversed business decline) for one internaonal PERS vendor; simultaneously created 2 segment businesses with personal branding (PERS & Tele-health); stabilized, then doubled health systems business (enterprise-level for hospitals & senior care integrator businesses); proven builder of profitable sales teams.

Life Warning’s world class expert -based call center team is comprised of global leaders in monitoring and processing. 24/7 emergency responsive care is provided through one of America’s elite rated 5 star CSAA diamond class facility backed by around the clock teams of SIA cerfied Emergency Medical Technicians. Life Warning’s customer care service is managed by a synergy of Life Warning team members working diligently around the clock to support customer needs.

Call Center

PRESIDENT

OPERATIONS

SALES

Leadership Experience

1580 Sawgrass Corporate Parkway Sunrise, FL 33323 USATel: 800-694-0006Fax: 855-687-9496Email: web@lifewarning.comwww.lifewarning.com

Life Warning, Inc.