Smart Mote-based Medical System for Monitoring and Handling Medication among Persons with Dementia

Victor Foo Siang Fook1, Jhy Haur Tee2, Kon Sang Yap2, Aung Aung Phyo Wai1, Jayachandran Maniyeri1, Biswas Jit1, Peng Hin Lee2

1 Institute for Infocomm Research {Sffoo, Apwaung, Mjay, Biswas}@I2r.a-star.edu.sg

2 Nanyang Technological University {Dunforget, 810924085345, Ephlee}@Ntu.edu.sg

Abstract. This paper presents a novel smart mote-based portable medical system which automatically monitors and handles medication among persons with dementia based on wireless multimodal sensors, actuators and mobile phone or PDA (Personal Digital Assistance) technology. In particular, we present the subtle design, implementation and deployment issues of monitoring the patient’s behavior and providing adaptive assistive intervention such as prompts or reminders in the form of visual, audio or text cues to the patient for medical compliance. In addition, we develop mobile phone or PDA applications to provide a number of novel services to the caregivers that facilitate them in care-giving and to doctors for clinical assessment of dementia patients in a context enlightened fashion.

Keywords: Mote, Medication, Persons with dementia, Mobile phone or PDA

1 Introduction

There is mounting worldwide interest to apply recent developments in context-aware systems, wireless sensor networks and mobile phone technology for healthcare. One area of focus is to develop activities-of-daily-living (ADL) behavior understanding system to facilitate caregiving and clinical assessment of demented elders within their homes. It is crucial for a physician to know whether the dementia patients are taking their daily medication at homes in order to prescribe the right dosage and to dispense correct advice on caring and coping to care-givers. This information is traditionally extracted from interviews with caregivers or even the patients themselves and suffers from serious problems of selective recall, knowledge gaps and inaccuracies. Hence, it is a huge challenge to physicians to promote patient adherence to the treatments.

However, failure in medical compliance will render the medical treatments ineffective and may lead to disastrous consequences. The father of medicine, Hippocrates, who already realized the importance of medical compliance more than twenty centuries ago once said: “The physician must not only be prepared to do what

is right himself, but also make the patient cooperate”. In this paper, we describe a novel smart mote-based portable medical system which automatically monitors and handles medication among persons with dementia at homes based on wireless multimodal sensors and mobile phone or PDA technology to promote medication adherence. It provides assistive cues to patients in the form of prompts and reminders, and allows physicians or caregivers to monitor patient’s medicine taking activity and obtain summarized behavioral reports from their PDA anytime, anywhere. Section 2 discusses the related works. Section 3 describes the design considerations and details of a smart medical system. Section 4 describes the PDA or mobile phone applications to the caregivers and doctors for caregiving and clinical assessment in a context aware fashion. Finally, section 5 concludes with a discussion of future works.

2 Related Works

Previously, many systems have been developed to support medication adherence. The assistive technology lab in University of Toronto proposes a medication reminding system [1] using context aware technology while ETH Zurich proposes a smart medicine cabinet [2] using passive RFID and Bluetooth-enabled active tags to monitor the contents of the box. The Lanchaster University [3] designs a device to support the management of medication in a community care environment, and the University of Ulster proposes a pill container [4] for medication. Recent works in Intel by Jay Lundell [5] proposes a smart context aware medication adherence system. Our work is similar to them in that we also use sensors, context aware systems and PDA or mobile phones to provide assistive cue to patients such as reminders for medical compliance. However, our work is different in some ways as we seek to provide a single holistic integrated portable smart medical system to collectively address and satisfy the different needs and perspectives of all stakeholders for medication compliance such as the patients, caregivers and doctors using the pervasive mote platform. In the long term, we hope to integrate more sophisticated behavior understanding system to provide holistic solutions to dementia patients beyond medication adherence using the popular mote platform.

3 Smart Medical System

In this section, we will describe our hardware and software design considerations of a smart medical system for monitoring and handling medication in dementia persons.

3.1 Design Considerations

We study the requirements from the perspectives of patients, caregivers and doctors, and also from the literature survey. In all cases, it requires the smart medical system to be safe but reliable enough to capture the medication taking behavior of patient. Good recognition rate must be achieved and false alarms should be minimized to

improve the practicality of mass deployment of such a system. It should not be intrusive and not change the behaviors of the dementia patients. Furthermore, the devices should be adaptable to the changing environments such as cases in which temperature can change due to weather or artificial cooling and hence the sensors in the system must be temperature compensated. From patient’s perspective, it should be portable so that a reminder can be sent to patients when they are not at home. The device should be easy to use or easily worn like a watch. Different video, audio and text cues may be provided for those with hearing problems, visual problems, etc and situated reminders should be sent as the dose might be missed due to sleeping.

From caregivers’ perspective, if the patients decide not to or forget to take medication, at least a automated monitoring system or additional form of support such as situated alert being sent for the caregivers will bring some bearing to assist with their non-compliance. Likewise, from doctors’ perspectives, the system should assist in the control of medication administration and drug therapy, and record the progress of the patient through a dedicated medication regime. In sum, the requirements of the smart medical system are two-fold: capture all the medication taking characteristic behavior relating to the dementia patients in a non-intrusive way, and intervene by processing and relaying information in a context-aware and distributed manner.

3.1.1 System Design The smart medical system is designed to meet the above considerations and consists of a medication box with sensors, patient medication analyzer, central server and PDA or mobile phones. It is designed such that a smart medicine box can be connected to multiple related doctors and caregivers, and vice versa.

The medicine box periodically sends sensor readings wirelessly to the patient medication analyzer. Using a PDA, a doctor can first authorize himself through the central server, and connect to the patient medication analyzer to obtain behavioral reports of the patient in a distributed manner. This system is illustrated in Figure 1.

Fig. 1. The smart medical system

The smart medical system provides scalable monitoring provisioning and support standardized schemes for automated intervention management and activity planning. The detailed hardware and software components are described in the next sections. 3.1.2 Hardware The wireless smart mote-based medicine box for medical adherence among persons with dementia is designed as shown in Figure 2.

Fig. 2. The smart medicine box implemented with various sensor technologies

It utilizes multimodal sensors and actuators to monitor and assist patient in taking medicine. The smart medicine box consists of nine drawers – seven drawers for each day in a week which allows the patient to differentiate the medicine to be taken for a particular day and two general ones. A LED will blink to assist the patient in opening the right drawer. If a wrong compartment is opened, an error tone will be generated to alert the patient. In case the patient forgets to take the medicine, a reminder tone will be generated to remind the patient it is time to take his medicine. The details of the hardware components are described below: • Motion Sensing Medicine Box Lid and Environmental Light Sensing A mote with accelerometer and light sensor is attached to the main lid of the medicine box to sense whether it is opened or not. The lid is detected as open if the accelerometer sensor readings drop by an amount greater than 25 and if the light sensor detects a light intensity greater than 700 (maximum reading is 1024). • Sensing Human Presence and Motion (PIR) An external PIR sensor connected to the mote is used to sense the presence of the patient around the box. Output of the PIR sensor is connected to the ADC port on the sensor board. When motion is detected, this output readings will go below 600. An ultrasonic sound sensor is also used to determine the distance of patient from the medicine box. Signal from an external ultrasonic sensor powered by a 6V voltage source is fed into the ADC1 port of the MTS101CA sensor board on the mote. The ADC port interprets the ultrasonic sensor readings in a range from 0 to 1024. If main lid is closed, the distance to obstacle will be very small. When the lid is opened, the

value will be very high (>200). Using a simple algorithm, the system reports the motion of patient in front when the readings are in the range from 20 to 100.

• Controlling LED Indicators When the main lid is opened, LED on the drawer to be opened will start blinking. Since there are only limited motes and PWR output ports available, we design a circuit so that the 7 LEDs can be turned on or off efficiently. 3 output ports on the sensor board of this mote, PW3, PW4, PW5, are used to control the LED indicators as shown in Figure 3. The 3 output voltages from the mote are connected to a 3 to 8 decoder. The outputs from the decoder are connected to the negative input of the 7 LEDs. The positive terminals of every LEDs are also connected to the output pin of a 555 timer, which generates a high and low voltage alternatively.

Fig. 3. LEDs control circuit schematic

• Sensing Drawer Motion A smaller mica2dot mote together with REED switches and a resistor circuit is used to achieve the above purpose. A circuit as shown in Figure 4 is implemented to sense the status of 9 drawers through 1 ADC port effectively. A reference voltage source supplies a series of 10 resistors in which 9 of the resistors are connected to 9 of the drawers and a resistor acts as a reference resistor. When a drawer is closed, a REED switch connected to it will be closed, and therefore its connected resistor will be short-circuited from the series. The voltage across the reference resistor will increase accordingly and can be used to detect whether the drawer is opened.

Fig. 4. Series resistor network to sense drawer status

• Tone Generation Tones are generated by two output voltages to the buzzer/speaker when a mote receives a control message from the patient medication analyzer. The controlling circuit is depicted in Figure 5 below.

Fig. 5. Circuit to generate reminder and error tones

3.1.3 Software We adopt a layered software architecture design to act as a platform for developers to build applications. Besides the NESC modules in the medication box, it consists of modules for the patient medication analyzer, central server and PDA/mobile phones. 3.1.3.1 Patient Medication Analyzer Modules The analyzer modules collect sensor readings from the medicine box and store them into a database. Servlets running in tomcat are implemented to perform task requested by the PDA/mobile phone and also many tasks such as reply current alarm settings in database upon request, reply a text report for a particular day, etc. A Bayesian reasoning engine is also integrated for performing information fusion between multiple sensors. Using Bayesian network, we can infer the probability of an event objectively based on the data collected. As shown in Figure 6, a Bayesian network is used to calculate the probability that a patient is taking medicine at a particular instance. In the network, node “Taking-medicine” acts as the parent node for door status, drawer status and human detection nodes. Its status will directly affect the values of the three children nodes. For instance, if a patient is taking medicine, there is a higher probability that the lid is opened, correct drawer is pulled out, and a patient is detected in the proximity. Light and accelerometer data are used to infer lid status, and ultrasound and infrared data are used to sense whether the patient is nearby to the medicine box. Meanwhile, the drawer sensor data is used to indicate the drawer being pulled out. Time node in the Bayesian network acts as a prior probability node of patient taking medicine. The prior probabilities can be hour-based which means that each hour of a day has different prior probability. Every patient has different regular time on taking medicine, and hence the prior probabilities are not predetermined. A one-week time period samples are collected and used to train the prior probabilities. The trained prior probabilities will indicate the most likely as well as the least likely time of the patient to take medicine.

Fig. 6. Inferring Probability of Taking Medicine using Bayesian Network

3.1.3.2 Central Server Modules The central server is designed to manage the relationships among multiple medicine boxes and their respective caregivers or doctors. It is also used to perform authorization and maintain patient information that can be retrieved by the doctor. 3.1.3.3 Application Modules Applications for the patients, caregivers and doctors are built on the platform and the details of the applications built on PDA/phone will be described in the next section.

4 PDA or Mobile Phone Applications

Java applications are developed for the doctors and caregivers to monitor the status of the medicine box or request behavioral report of a particular patient. It can be easily extended to act as reminders. The functionalities include authentication and patient selection, change alarm settings, request for live report, etc.

A login interface is presented for the user and once authorized by the central server, one has the options to view patient particulars, register a new patient into the system or update login information. The application will also show a menu which consists of four major features developed: ‘Set Reminder’, ‘Live Report’, ‘Text Report’ and ‘Graph Report’ as shown in Figure 7.

Fig. 7. Interface for caregivers or doctors

Feature 1: Set Reminder Set Reminder feature allows one to remotely set 5 reminders for the medicine box. A request was first sent to the Analyzer to obtain current alarm settings, and then new settings will be sent to the servlets to update the database records. Feature 2: Live Report Live report feature allows user to view real-time status of the medicine box. Two options are available: Chart Mode or Image Mode as shown in Figure 8. For Chart mode, latest sensor readings are updated on the chart while Image Mode allows the user to gain pictorial view on the status of the medicine box in real time.

Fig. 8. Live Report – Chart View and Image View

Feature 3: Text and Graph Report Text report shows summarized behavioral report of a patient in daily, weekly and monthly format generated based on Bayesian networks. Daily Report shows time slots when probability of taking medicine by patient is high by inferring it from the percentage of times the correct drawer is opened. Weekly and Monthly report conclude total number of times that the patient has taken or not taken medicine in weekly and monthly basis. Detailed Log for Daily Report shows peak periods when probability of medication activity is high, sensor modalities that contributed to the probability and the drawers opened at a particular period, and Graph Report displays full probability information of selected day in graphical form, as depicted in Figure 9.

Fig. 9. Text and Graph Report.

Experiments are conducted by students to simulate patient’s behavior and the results are encouraging that we will try to deploy the system in patient’s home for evaluation. While development is still in its early stages, our joint effort with a local hospital should see us achieving our objective of validating it in a real life setting.

5 Conclusion

We present a smart medical system for monitoring and handling medicine taking behaviors for dementia patients, caregivers and doctors. The use of multimodal sensors, actuators and PDA or mobile phone is the first step for us to promote medical compliance, and we are now furthering our work by including more sensor modalities such as pressure sensors, RFID, etcr. to enhance the recognition rate and reduce false alarm, and also working on adaptive interface such as LED status board for patients. The joint effort with a local hospital should see us achieving our long term objective of integrating more sophisticated behavior understanding system to provide holistic solutions to dementia patients beyond medication adherence using the mote platform.

References

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5. L. Jay et al., Why Elders Forget to Take Their Meds: A Probe Study to Inform a Smart Reminding System, 4Th International Conference on Smart Homes and Health Telematics, (2006)