2011 Fifth International Conference on Sensing Technology
RF Hand Gesture Sensor for Monitoring of Cigarette Smoking Edward Sazonov*, Kristopher Metcalfe and Paulo Lopez-Meyer
Stephen Tiffany Department of Psychology The State University of New York at Buffalo Buffalo, NY, 14260, USA
Department of Electrical and Computer Engineering The University of Alabama Tuscaloosa, AL, 35487, USA *
[email protected]
Abstract—Today, over a billion people in the world are smokers. Smoking is associated with increased risk of cardiovascular disease, chronic obstructive pulmonary disease, emphysema, and various cancers, causing approximately 6 million premature deaths per year. Current methods of assessing smoking behavior (e.g., self-report, portable puff-topography instruments) do not permit the collection of accurate, non-reactive measures that capture real-time smoking frequency and comprehensive withincigarette puff topography. Our goal is development of a noninvasive wearable sensor system (Personal Automatic Cigarette Tracker – PACT) that is completely transparent to the end user and does not require any conscience effort to achieve reliable monitoring of smoking behavior in free living individuals. A key component of PACT is a sensor that captures a characteristic hand-to-mouth gesture preceding cigarette smoke inhalations. This paper details design and validation of a wearable radiofrequency proximity sensor that measures the distance between an individual’s wrist and chest in real-time. Hand-to-mouth gestures detected with this device provide quantitative data that can be used for analysis of behavioral patterns during smoking and other activities. Keywords-smoking; hand gesture; sensor
I.
INTRODUCTION
The World Health Organization estimates that more than a billion people worldwide are smokers, and tobacco use is rising globally, mostly in developing countries [1]. Smoking is a significant risk factor for development of several types of cancer (lung, mouth and larynx, pancreatic and bladder), cardiovascular disease (heart attacks and stroke) and pulmonary disease (chronic obstructive pulmonary disease and emphysema). The “tobacco epidemic” is a cause of preventable death for 6 million people per year, of which 600,000 are non-smokers that die from exposure to tobacco smoke [2]. More than 80% of tobacco-related deaths occur in developing counties, mostly due to aggressive marketing, low prices and lack of awareness of tobacco danger. Cigarette smoking is probably the most prevalent form of tobacco use. According to 2009 National Survey on Drug Use and Health conducted in the United States, 58.7 million persons (23.3 percent of the population) were current cigarette This work was partially supported by a grant DA029222 from the National Institute on Drug Abuse.
978-1-4577-0167-2/11/$26.00 ©2011 IEEE
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smokers; 13.3 million (5.3 percent) smoked cigars; 8.6 million (3.4 percent) used smokeless tobacco; and 2.1 million (0.8 percent) smoked tobacco in pipes [3]. Understanding of behaviors associated with cigarette smoking, such as frequency of smoking and smoke exposure (for example, depth of inhalation and duration of smoke holding), is important for evaluating and improving effectiveness of behavioral and pharmacological smoking interventions. Current methods of monitoring of cigarette smoking in free living individuals include self-report and portable puff topography instruments. The accuracy of self-report is limited due to memory limitations of individuals and intentional misreport. Portable puff topography instruments allow for collection of rich information on puffing behavior but offer limited accuracy on assessing frequency of smoking due to the fact that, to be detected, a cigarette has to be smoked through the instrument. To address these limitations, our major goal is development of a non-invasive wearable sensor system (Personal Automatic Cigarette Tracker - PACT) that is completely transparent to the end user and does not require any conscience effort to achieve reliable monitoring of smoking behavior in free living individuals. A major component of PACT is a hand gesture sensor that detects a characteristic hand-to-mouth gesture that precedes most of the cigarette puffs. The hand gesture sensor should reliably detect proximity of a subject's wrist to their chest and objectively capture characteristics of hand movements to determine the frequency and duration with which these gestures are being performed. The sensor data then can be combined with signals from other sensors in order to isolate the purpose of the gesture. This means that more complex activities such as food intake, smoking, or other actions that involve this motion can be detected in whole or in part by this sensor. Reported approaches to detecting hand gestures include a variety of methods. In [4] accelerometers were used to detect velocity of movements. The authors of [5] used infrared range detectors to detect specific directional movements. Additionally, capacitive sensing [6] and video [7] have been used to create detailed data about exact position. These sensors can be extremely accurate and versatile, however they cannot provide the exact functionality needed for all situations. The use of accelerometers only provides information related to
movement and not the exact position of the arm and thus offers only a limited functionality. The other methods were not implemented in a way that allows the subject to freely go about their normal activities while measurements are collected. This paper proposes a RF-based hand gesture sensor that detects the proximity of the hand to the chest and is also portable enough that it does not impair movement in any way. This sensor uses two battery-powered circuits, a receiver, and a transmitter that communicate using low frequency radio signals and act together to detect the distance between them by measuring signal strength. The proposed sensor was validated in a human study testing its sensitivity to hand gestures associated with common every day activities and smoking. II.
a)
b)
Figure 1. a) The concept of a hand gesture sensor. Miniature transmitter ❶ is placed on the dominant hand. Signal strength is measured by antenna ❷. b) A subject wearing the hand gesture sensor.
SENSOR SYSTEM
A. Concept The concept and a practical implementation of the hand gesture sensor is shown in Figure 1. A small transmitter is placed on the dominant hand of the subject. The transmitter emits RF frequency that is received by a flat loop antenna attached to the chest of the subject. Under idealized assumptions of isotropic emission of electromagnetic energy, the received signal strength will be proportional to the square of the distance from source d. Given the ∞–shaped directional sensitivity of loop antennas used in the proposed sensor, the signal strength will also depend on relative orientation of the transmitter and receiver antennas. The proposed design utilizes antenna directionality to obtain a higher magnitude of the signal strength from smoking gestures by placing the transmitter antenna on the inner side of lower arm. A typical cigarette holding gesture (Figure 1a) will result in almost parallel alignment of the antennas and, thus, the highest magnitude of the signal strength. Other hand gestures (Figure 2) from eating and other activities will have the antennas positioned at an angle and thus result in lower amplitude at the same distance. While the amplitude by itself is probably not sufficient to differentiate hand-to-mouth gestures of various origins, it provides one of the features that can be used for classification.
B. Transmitter The transmitter is a simple sine wave oscillator operating at 125kHz (Figure 3). This circuit uses a loop antenna manufactured by Sonmicro (40x15x5 mm, 860uH ±%10, 13ohm) as an inductor to transmit a radio signal. The oscillator circuit and antenna are contained in a plastic enclosure positioned on a wrist band. The transmitter circuit consumes approximately 4mA @ 3.3V, allowing for over 37 hours of continuous operation from a 150 mAh Li-ion battery. Minimal size and weight (6g) of the transmitter create virtually no interference with normal activities and are comparable to a typical hand watch. C. Receiver The schematic of the receiver is shown on Figure 4. A custom-made antenna (110x110x5mm, 1080uH, 8.3ohm) is positioned on subject’s chest using a Velcro strip. The antenna was designed using standard calculation techniques [9] and has a higher aperture than the transmitter. The larger size allows a reduction in differences in sensor reading due to variability of hand gestures (resulting in slightly different positioning of the transmitter). The rectified RF signal is amplified, low-pass filtered, and sent to the input of a datalogger or a DAQ card.
The transmitter-receiver pair operates on the 125kHz frequency. This frequency is already commonly used for RFID, and the low transmission power of this sensor allows compliance with FCC and global standards for interference and range [8].
Figure 2. Examples of hand gestures not associated with smoking show that transmitter antanna orientation in many cases is almost orthogonal to the the receiver.
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Figure 4. Reciever schematic. Figure 3. Transmitter schematic.
III.
The sensor signal was analyzed for any deviations from the baseline that exceeded 0.1V threshold.
METHODS
A. Antenna Tuning In order for this sensor to operate properly, the resonating circuits must be tuned to respond maximally at 125kHz. By properly tuning the antennas, the greatest possible range and accuracy can be achieved. An inductance meter is recommended for accurately calculating the resonant frequency; however, it is necessary to experimentally verify that the optimal tuning has been achieved. By introducing a sizable piece of ferrite or flat aluminum foil to the magnetic field produced by the antenna, its inductance can be temporarily changed [10]. In this manner, changes in amplitude were easily seen on an oscilloscope, and the circuits were adjusted accordingly by changing the number of turns of the loop antenna or adjusting a variable capacitor. B. Calibration During a typical smoking gesture, the transmitter coil is positioned within 10cm from the receiving antenna. With parallel and center-aligned antenna positioning, such proximity should result in the maximum output (3.0V). The sensitivity of the circuit was adjusted to the desired level by varying the amplification gain (Figure 4). A calibration curve for distance as a function of signal strength was obtained by varying the distance between parallel, center-aligned antennas from 1 to 50cm. C. False postive testing Because the proposed sensor design relies on a RF frequency range typically associated with RFID circuits, it may be susceptible to interference from various RFID equipment as well as other sources of electromagnetic interference. In order to analyze the effect of external electromagnetic interference into the system resulting in potential false positives detection (registering external interference as a hand gesture), the RF receiver was worn without matching RF transmitter in three different days for a total of 20+ hours of recordings during daily activities in free-living conditions like walking and driving in the city, doing laundry, and operating different house-hold appliances such as a microwave and a vacuum cleaner, which are known sources of electromagnetic interference.
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D. Testing on common activities Four regular smokers (carbon monoxide from a breath sample >10ppm), two males and two females, ages 24.3±5.3 with BMI of 28.52±9.01, were enrolled in an experiment where they were asked to wear the described hand gesture sensor while being videotaped. The study was approved by the IRB at the University of Alabama and informed consent was received from all subjects. While wearing the sensor, the subjects were asked top engage in different activities: sit comfortably, read aloud, stand in a still position, walk at different speeds on a trend mill, eat a meal using their hands, and use silverware, walk outdoors, smoke a cigarette while sitting, sit comfortably and, finally, smoke a cigarette in standing position. A portable data logger (Logomatic V2.0, Sparkfun Electronics) recorded the proximity signals captured by the RF receiver at a 100 Hz sample rate and stored them in a microSD card. The stored signal was manually analyzed with a specially designed scoring software that allowed simultaneous review of the sensor signal and time-synchronous video, and marking up of the events. All visually detectable hand gestures were marked on the sensor signal and statistics on number of true positives, false negatives, and duration and amplitude of the hand gesture signal associated with smoking and other activities was computed. IV.
RESULTS
After tuning the antennas using the procedure discussed above, the calibration curve shown in Figure 5 was obtained. False positive testing demonstrated no occurrences of the sensor signal exceeding a 0.1V threshold, thus indicating no false positives. Figure 6 illustrates signal from hand gesture sensor obtained on a single subject performing common everyday activities. The statistics on duration of hand gestures associated with smoking and all other activities are shown in Table I and statistics on peak amplitude of the signal are shown in Table II. Counts of true positives (TP) and false negatives (FN) for each cigarette-to-mouth gesture were TP=98 out 98 and FN=0 out of 98.
system. The sensor demonstrated no false positives during testing in a noisy urban environment, which indicates good tolerance to electromagnetic interference. Further improvement is possible by incorporating a digital signature into the transmitted signal, effectively making the sensor an active RFID proximity tag. This work is ongoing. The sensor possessed high sensitivity up to approximately 20cm range (Figure 5) which is sufficient for reliable identification of hand-to-mouth gestures. For parallel colocation of antennas, the output is saturated until the distance of 17cm and then declines according to the inverse square of the distance. At the distance of 35 cm the sensor output is virtually zero and therefore not sensitive to most arm movements not associated with proximity to the mouth.
Figure 5. Sensor signal amplitude vs. distance.
TABLE I.
As expected, the sensor also captured hand gestures associated with other activities. Figure 6 illustrates that the activity generating the most hand gestures is eating, which indicates that this sensor may also be used in applications of Monitoring of Ingestive Behavior [11,12]. Interestingly, an episode of sitting (activity 11 on Figure 6) also generated what may be considered one long hand gesture, which resulted from subject holding his hand next to his head (Figure 2).
HAND GESTURE DURATION Duration, s Standard Deviation
Average
95% Confidence Interval
Smoking
2.77
1.45
0.46
6.4
All other activities
2.69
13.49
2.04
14.56
TABLE II.
HAND GESTURE AMPLITUDE Amplitude, % of the full range
Average
Standard Deviation
Smoking
81.84
21.1
28.41
100
All other activities
20.85
27.54
0
95.91
V.
95% Confidence Interval
DISCUSSION
The proposed sensor was very effective in identifying hand gestures originating from smoking and other activities. Specifically, the sensor correctly detected all hand-to-mouth gestures associated with cigarette smoking with no false negatives. This capability of the sensor is of prime interest for our research and will constitute a key component of the PACT
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Analysis of the statistics of the hand gesture sensor signal shows that, while duration of a gesture is very similar between smoking and other activities, the amplitude resulting from a cigarette-to-mouth gesture is substantially higher. This feature is attributed to ∞–shaped sensitivity of loop antenna, which, in combination with antenna placement on the inner surface of lower arm and chest, provides a higher degree of antenna parallelism during smoking gesture. While this feature alone probably will not be sufficient to differentiate between smoking and non-smoking gestures, it can be used in combination with other sensors in the PACT system to differentiate smoking hand gestures from others. Overall, the sensor design satisfied the needs of the PACT system. The hand transmitter is minimally obtrusive and comparable in weight and size to a hand watch. The receiver antenna and electronics easily incorporate into a wearable undergarment that contains other components of PACT.
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Figure 6. Hand gestures detected by the RF receiver-transmitter sensor for different activities of a single subject: 1) sitting, 2) reading aloud, 3) standing, 4) walk slow, 5) walk fast, 6) use PC, 7) eat with hands, 8) eat with silverware, 9) walk outdoors, 10) smoking while sitting, 11) sitting, 12) smoking while standing.
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VI.
CONCLUSIONS
The RF hand gesture sensor described in this paper is the first step in developing a reliable hand gesture sensor to be used in a non-invasive system for monitoring of smoking behavior. The sensor provides reliable identification of cigarette-to-mouth gestures with no false positives or false negatives. Potentially, this sensor can be incorporated into other applications such as monitoring of ingestive behavior. Future work on the development of this sensor will include incorporation of a digital signature and experimentation with antenna geometries that provide the highest sensitivity to gestures resulting from cigarette smoking. REFERENCES [1]
“WHO | WHO Report on the Global Tobacco Epidemic, 2009” Available: http://www.who.int/tobacco/mpower/2009/gtcr_download/en/index.htm l. [2] “WHO | 10 facts on the tobacco epidemic and its control” Available: http://www.who.int/features/factfiles/tobacco_epidemic/en/index.html. [3] “National Survey on Drug Use and Health (NSDUH)” Available: https://nsduhweb.rti.org/. [4] B. Graham, “Using an Accelerometer Sensor to Measure Human Hand Motion,” Massachusetts Institute of Technology, 2000. [5] Infrared Gesture Sensing, Silicon Labs: 2011. [6] “Non-contact and non-attached human hand motion sensing technique for application to the human machine interface,” SICE Annual Conference 2010, Proceedings of, Taipei: 2010, pp. 3536-3539. [7] “Visual interpretation of hand gestures for human-computerinteraction: a review,” Pattern Analysis and Machine Intelligence, IEEE Transactions on, vol. 19, Jul. 1997, pp. 677-695. [8] e-CFR: Title 47: Telecommunication. Available: http://ecfr.gpoaccess.gov/cgi/t/text/textidx?c=ecfr&sid=777c5291d1e08efdf5c3a2bd16169998&rgn=div5&vie w=text&node=47:1.0.1.1.14&idno=47 [9] F.W. Grover, Inductance Calculations: Working Formulas and Tables, Instrumentation Systems &, 1982. [10] ID Series Datasheet, ID Innovations, 2005. Available: www.sparkfun.com/datasheets/Sensors/ID-12-Datasheet.pd [11] E. Sazonov, O. Makeyev, P. Lopez-Meyer, S. Schuckers, E. Melanson, and M. Neuman, “Automatic detection of swallowing events by acoustical means for applications of monitoring of ingestive behavior,” Biomedical Engineering, IEEE Transactions on, vol. 57, Mar. 2010, pp. 626-633. [12] E.S. Sazonov, S.A.C. Schuckers, P. Lopez-Meyer, O. Makeyev, E.L. Melanson, M.R. Neuman, and J.O. Hill, “Toward Objective Monitoring of Ingestive Behavior in Free-living Population,” Obesity, vol. 17, May. 2009, pp. 1971-1975.
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