Voice-Activated Emergency Response: Integrating Arduino Nicla Voice with MIT App Inventor for Elderly Care in Smart Homes

Wei-Tzu Chin

doi:10.54097/ywvnmk24

Authors

Wei-Tzu Chin

DOI:

https://doi.org/10.54097/ywvnmk24

Keywords:

Personal Emergency Response Systems(PERS), Arduino Nicla Voice, MIT App Inventor, BLE Communication, Machine Learning in Healthcare, Elderly Care Technology, Voice Recognition Technology.

Abstract

Nowadays, the average lifespan of people is increasing steadily. A multitude of elderly people have the tendency to reside in their own preferred dwellings. However, as a result of this, it might be too late to discover some elders' sudden abnormal situations. Therefore, it is a great concern for them to instantly and effectively deliver their distress signals to the outside world under consciousness, due to the fact that unanticipated accidents may happen all at once. This proposed system integrates the embedded system “Arduino Nicla Voice” in detection with MIT App Inventor in display on the user's interface and deploys Bluetooth Low Energy(BLE) as the communication channel. First, 8 different values of window overlap were tested and the best outcome was used in the upcoming validation method. The overall system performance in terms of accuracy, precision, recall, loss, and F1-score were examined with a 10-fold stratified cross validation. A Receiving Operating Characteristic(ROC) graph and Area Under the ROC Curve(AUC) based on predicted probability were exhibited to see the performance of each fold and the micro-average as well. Then, limitations were discussed so as to improve the robustness of this PERS system for future studies.

Downloads

Download data is not yet available.

References

World Health Organization: WHO. Ageing and Health. 1 Oct. 2022, www.who.int/news-room/fact-sheets/detail/ageing-and-health.

“Indicators.” WHO Data, platform.who.int/data/maternal-newborn-child-adolescent-ageing/indicator-explorer-new/mca/percentage-of-total-population-aged-60-years-or-over.

Wiles, Janine, et al. “Older People and Their Social Spaces: A Study of Well-being and Attachment to Place in Aotearoa New Zealand.” Social Science & Medicine, vol. 68, no. 4, Feb. 2009, pp. 664–71. https://doi.org/10.1016/j.socscimed.2008.11.030.

---. Falls. 26 Apr. 2021, www.who.int/news-room/fact-sheets/detail/falls#:~:text=Each%20year%20an%20estimated%20684,medical%20attention%20occur%20each%20year.

---. From Walking Canes to Wrist Alarms: WHO’s Global Survey on Assistive Technologies. 11 Feb. 2016, www.who.int/news-room/feature-stories/detail/from-walking-canes-to-wrist-alarms-who-s-global-survey-on-assistive-technologies.

Mihailidis, Alex, et al. “The Acceptability of Home Monitoring Technology Among Community-Dwelling Older Adults and Baby Boomers.” Assistive Technology, vol. 20, no. 1, Mar. 2008, pp. 1–12. https://doi.org/10.1080/10400435.2008.10131927.

Popescu, Mihail, et al. “An acoustic fall detector system that uses sound height information to reduce the false alarm rate.” 2008 30th Annual International Conference of the IEEE Engineering in Medicine and Biology Society, 2008, https://doi.org/10.1109/iembs.2008.4650244.

Rougui, J.E., et al. “Audio sound event identification for distress situations and context awareness.” 2009 Annual International Conference of the IEEE Engineering in Medicine and Biology Society, 2009, https://doi.org/10.1109/iembs.2009.5334581.

Hamill, Melinda, et al. “Development of an Automated Speech Recognition Interface for Personal Emergency Response Systems.” Journal of Neuroengineering and Rehabilitation, vol. 6, no. 1, July 2009, https://doi.org/10.1186/1743-0003-6-26.

Istrate, Dan, et al. “Embedded Implementation of Distress Situation Identification Through Sound Analysis.” HAL (Le Centre Pour La Communication Scientifique Directe), July 2007, hal.science/hal-00957417.

Fiorini, Laura, et al. “Unsupervised Machine Learning for Developing Personalised Behaviour Models Using Activity Data.” Sensors, vol. 17, no. 5, May 2017, p. 1034. https://doi.org/10.3390/s17051034.

Lecouteux, Benjamin, et al. “Distant speech recognition in a smart home: Comparison of several multisource ASRS in realistic conditions.” Interspeech 2011, 2011, https://doi.org/10.21437/interspeech.2011-604.

Fang, Hongqing, et al. “Influence of Time and Length Size Feature Selections for Human Activity Sequences Recognition.” ISA Transactions, vol. 53, no. 1, Jan. 2014, pp. 134–40. https://doi.org/10.1016/j.isatra.2013.09.001.

Vacher, Michel, et al. “Complete Sound and Speech Recognition System for Health Smart Homes: Application to the Recognition of Activities of Daily Living.” InTech eBooks, 2010, https://doi.org/10.5772/7596.

Fleury, Anthony, et al. “SVM-Based Multimodal Classification of Activities of Daily Living in Health Smart Homes: Sensors, Algorithms, and First Experimental Results.” IEEE Transactions on Information Technology in Biomedicine, vol. 14, no. 2, Mar. 2010, pp. 274–83. https://doi.org/10.1109/titb.2009.2037317.

Kwon, Ohbyung, et al. “Single Activity Sensor-based Ensemble Analysis for Health Monitoring of Solitary Elderly People.” Expert Systems With Applications, vol. 39, no. 5, Apr. 2012, pp. 5774–83. https://doi.org/10.1016/j.eswa.2011.11.090.

Hossain, M. Shamim. “Patient status monitoring for smart home healthcare.” 2016 IEEE International Conference on Multimedia & Expo Workshops (ICMEW), 2016, https://doi.org/10.1109/icmew.2016.7574719.

Hussain, Akhtar, et al. “Health and Emergency-care Platform for the Elderly and Disabled People in the Smart City.” Journal of Systems and Software, vol. 110, Dec. 2015, pp. 253–63. https://doi.org/10.1016/j.jss.2015.08.041.

Gayathri, K., and K. S. Easwarakumar. “Intelligent Decision Support System for Dementia Care Through Smart Home.” Procedia Computer Science, vol. 93, Jan. 2016, pp. 947–55. https://doi.org/10.1016/j.procs.2016.07.281.

Gayathri, K. S., et al. “Activity recognition and dementia care in Smart Home.” Advances in Theory and Practice of Emerging Markets, 2018, pp. 33–47, https://doi.org/10.1007/978-3-319-78378-9_2.

Chen, Jingcheng, et al. “Improving Human Activity Recognition Performance by Data Fusion and Feature Engineering.” Sensors, vol. 21, no. 3, Jan. 2021, p. 692. https://doi.org/10.3390/s21030692.

Arduino. Arduino® Nicla Voice. Arduino, n.d. Web. Accessed 31 Oct. 2023. https://docs.arduino.cc/resources/datasheets/ABX00061-datasheet.pdf.

Nordic Semiconductor. nRF52832 Product Specification v1.4. 10 Oct. 2017, https://infocenter.nordicsemi.com/pdf/nRF52832_PS_v1.4.pdf.

Syntiant. Syntiant Product Brief NDP120. n.d., https://static1.squarespace.com/static/6488b0b8150a045d2d112999/t/650b29219d8c3f0e27f4651e/1695230242406/Syntiant-Product_Brief_NDP120.pdf.

u-blox. ANNA-B112 DataSheet UBX-18011707. 14 Mar. 2023, https://content.u-blox.com/sites/default/files/ANNA-B112_DataSheet_UBX-18011707.pdf.

Bluetooth SIG. The Bluetooth LE Primer V1.1.0. 17 Jan. 2023, https://www.bluetooth.com/wp-content/uploads/2022/05/The-Bluetooth-LE-Primer-V1.1.0.pdf?__hstc=44473531.3ccd99af25786010176b1818f9f3a3d2.1702609437500.1702609437500.1702609437500.1&__hssc=44473531.5.1702609437500&__hsfp=1214847747&hsCtaTracking=8e3cb9ce-2e7b-471a-b5cc-2343a4915b6a%7C090f705a-f0df-4f4b-8a54-c8f97c73eb69.

Singh, Deepika, et al. “Ambient Assisted Living Technologies From the Perspectives of Older People and Professionals.” Lecture Notes in Computer Science, 2017, pp. 255–66. https://doi.org/10.1007/978-3-319-66808-6_17.