Smart Bracelets: Towards Automating PersonalSafety using Wearable Smart Jewelry

Jayun Patel, Ragib HasanDepartment of Computer Science,

The University of Alabama at Birmingham, AL, USA.Email: (jayun18, ragib)@uab.edu

Abstract—Ensuring personal safety and detecting physicalassaults are critically important issues. During an assault, victimsoften have no time to call for help using their mobile phones.Most panic button type emergency call devices also requireactively pressing a button to contact emergency services. Therequirement of active intervention by the victim (through dialing911 or pressing a button) reduces the effectiveness of the servicefor ensuring personal safety. To resolve this problem, we usethe notion of a smart wearable device, in the form of a smartjewelry bracelet, to automatically sense, detect, and identifyphysical assault. The smart bracelet uses a multitude of sensorsand machine learning to detect an assault as it takes place andthen proceeds to contact emergency services and take a seriesof protective actions. In this demo, we will demonstrate thesmart jewelry bracelet and show its usability and effectivenessin providing a low-cost, practical, and usable tool for preventingphysical assault and attacks, as well as helping elderly users.

Keywords-Wearable health, Mobile Device, Wearable Device

I. INTRODUCTION

Physical violence, especially against women and children,is an unfortunate social problem. This is a world wide problemwhich is not limited to any specific regions. According tothe World Health Organization, 35% of women world wideexperienced physical or sexual violence [1]. Even in countriesin the developed world, physical violence against women,children, and the elderly are unfortunately far too common.

A major challenge in preventing physical violence is theinability of the victims to seek timely help as the assaulttakes place. Even victims who carry mobile phones maynot have enough time to call for help. The assault can beunexpected, sudden, and incapacitating, rendering the victimincapable of getting their phones out and dialing for help.Women often carry their phones inside a purse and the severalseconds it takes to get the phone out gives the attacker anedge for assaulting the victim and disabling the victim’saccess to the phone. Surprise attacks also result in the victimbeing incapable of calling for help. Elderly victims may haveslower reflexes that reduces their reaction times. The “panicbutton” type devices, which provide a one-click access tosecurityservices, also suffer from the same usability problems[2, 3]. All these issues imply that any solution to detect andreport assaults should ideally be automated, where the victimsdo not have to actively press buttons or dial a phone to get help.A better and more effective solution would be to use machinelearning and user movement analysis to identify potentialassaults and contact the emergency services automatically,without requiring the user’s action.

To resolve this, we have developed the Smart JewelryBracelet – a wearable smart device embedded in a regu-lar fashion bracelet. It contains an Adafruit Flora controller(Arduino-compatible), equipped with a gyroscope, accelerom-eter, temperature and pressure sensors, GPS, and microphones.The smart jewelry bracelet collects user activity and vitalsigns data continuously from the sensors. A machine learningalgorithm running on the Adafruit detects and differentiatesthe user’s regular movement and unexpected and suddenmovements indicative of an assault. On detecting an assault,the bracelet then connects to the user’s phone over Bluetoothand contacts the emergency services automatically via an App.The system is also applicable in detecting debilitating falls forelderly people. In our demonstration, we will show how thewhole process is automatic and does not require active userintervention to seek help from emergency services.The entiresystem fits within a regular fashion bracelet which is essentialto ensure usability and adoption.

The Smart Jewelry Bracelet provides a usable, low cost,and effective way to detect sudden and unexpected physicalassault and contact the emergency services for help in a timelymanner. The machine-learning enabled automated assault de-tection mechanism would lead to a more pragmatic approachtowards dealing with physical assaults.

Finally, the design of this smart jewelry bracelet incorpo-rates user studies and field testing. Many wearable devicessuch as smart glasses or smart watches are often seen as nerdy[4], which often reduces their appeal to the teenagers or youngadults. Instead of inventing a stand-alone device to resolve theassault problem, we chose to make jewelry that people weareveryday into smart and sensory capable wearable devices. Byincorporating a sense and compute element to regular jewelrydesign, we built the smart jewelry bracelet which was moreattractive and easy to adopt by the teenagers and young adultsinvolved in our user study.

II. MOTIVATING SCENARIO

We designed the Smart Jewelry Bracelet to be mainly usefulfor detecting assault. However, besides assault detection, itcan have multiple usages. We describe several usage scenariosbelow which we plan to show in our demonstration.

Automated Assault Detection: Alice is wearing the SmartJewelry Bracelet. As she is walking back from a party, sheis suddenly assaulted from behind. As she is struggling withthe attacker, the smart bracelet detects her movements as anassault and it triggers her phone to call 911 to seek help. It

(a) AdaFruit Flora (b) A user wearing the smart bracelet (c) Testing to detect assault

Fig. 1: Smart Jewelry Bracelet

also sends her location to the police via a text message routedthrough the phone. The paired app connected to the braceletcontinues to transmit her location to the police. Due to thetimely notification and location information, Alice is easilyrescued by the police.

Support for Elderly or Disabled Users: Besides assault, thebracelet can also be used by the elderly or disabled people toautomatically detect sudden falls or other risky movements. Inthis scenario, Edna, a senior citizen is wearing a smart bracelet.She suddenly suffers a fall while walking outside. She cannotreach for her phone. However, the bracelet detected the fall asan unusual movement and it immediately called 911 for help,while transmitting her location. With this information, rescueworkers are able to locate and help Edna.

Of course, in both of these cases, a phone call could havesolved the problem. However, in the first scenario, Alicecannot make the call as she is struggling with the surpriseattacker. In the second scenario, Edna cannot call due to beingdisabled from the sudden fall. While there are panic buttontype systems for both scenarios, they are ineffective while thevictim is struggling with an attacker or when the victim isdisabled or unconscious.

III. DESIGN

Components: The Smart Jewelry Bracelet consists of thefollowing components: (1) Sensors; (2) Analyze and classifierunit; (3) Communication module; (4) Native display module;and (5) a Phone-based App Agent. The AdaFruit controller hasembedded 3-axis accelerometer, gyroscope, flex sensor, andtemperature sensor. The sensor data stream is fed into the An-alyze and classifier module, which is run on the AdaFruit Floracontroller (Figure 1a) and is written in Arduino-compatible Clanguage. An LED Matrix included in the AdaFruit can beused to convey information, while a speaker module can playa siren or loud noise. The Analyze and classifier module runsmachine learning software that can classify and distinguish be-tween regular movement and potential assault. Once triggered,the module uses the Communication module to communicate

with the phone-hosted app agent over Bluetooth. The agentapp then uses the phone to call 911 or send text and also cantransmit the user’s GPS coordinates to the responders. We usedoff-the-shelf components such as AdaFruit Flora to build thehardware platform. The components are small enough to behidden inside a regular fashion bracelet (See Figures 1b and1c). The whole system was powered by a small battery packplaced in the band of the bracelet.

IV. CONCLUSION AND DEMONSTRATION PLANSIn this demo, we introduce the notion of automated physical

assault detection using the Smart Jewelry Bracelet. It candifferentiate between regular movements and struggles relatedto assaults, thus automating the problem of calling for helpduring a surprise assault. Through our demonstration, we aimat showing the utility and usability of the Smart JewelryBracelet. We will also show the various use cases as well asthe technology behind the bracelet. A smart jewelry braceletwould usher in a new area of research and development –taking into account usability and benefits achieved by wearablesensory devices, and at the same time will be highly beneficialto potential victims of physical assault or to elderly users.

ACKNOWLEDGEMENTSThis research was supported by the National Science Foun-

dation CAREER Award CNS-1351038 and ACI-1642078. Theauthors would like to thank Uzma Noor, Sabirah Haque, andRahul Parikh for their insights and help with prototype testing.

REFERENCES[1] World Health Organization, “Global and regional estimates of violence

against women: prevalence and health effects of intimate partner violenceand non-partner sexual violence,” 2013.

[2] M. Chan, D. Esteve, J.-Y. Fourniols, C. Escriba, and E. Campo, “Smartwearable systems: Current status and future challenges,” Artificial intel-ligence in medicine, vol. 56, no. 3, pp. 137–156, 2012.

[3] Y. Silina and H. Haddadi, “New directions in jewelry: A close look atemerging trends & developments in jewelry-like wearable devices,”in Proceedings of the 2015 ACM International Symposium on WearableComputers, ser. ISWC ’15. New York, NY, USA: ACM, 2015, pp.49–56. [Online]. Available: http://doi.acm.org/10.1145/2802083.2808410

[4] L. Brown, “The development of a mobile smart watch application utilisinga heart rate monitor to introduce users to the practice of mindfulmeditation,” Ph.D. dissertation, Cardiff Metropolitan University, 2016.