# A Smartphone-based Architecture for Prolonged Monitoring of Gait

Louise Bart, El Amine Bechorfa, Antoine Boutet, Jan Ramon, Carole Frindel

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Louise Bart, El Amine Bechorfa, Antoine Boutet, Jan Ramon, Carole Frindel. A Smartphone-based Architecture for Prolonged Monitoring of Gait. AIMHC 2024 - IEEE First International Conference on Artificial Intelligence for Medicine, Health and Care, Feb 2024, Laguna Hills, United States. pp.16-17, 10.1109/AIMHC59811.2024.00010. hal-04909717

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# A Smartphone-based Architecture for Prolonged Monitoring of Gait

Louise Bart

Univ Lyon, INSA Lyon

CREATIS, Inserm

Lyon, France

louise.bart

@insa-lyon.fr

El Amine Bechorfa

Univ Lyon, INSA Lyon

Inria

Lyon, France

mohamed.bechorfa

@inria.fr

Antoine Boutet

Univ Lyon, INSA Lyon

Inria, CITI

Lyon, France

antoine.boutet

@insa-lyon.fr

Jan Ramon

Inria

Lille, France

jan.ramon

@inria.fr

Carole Frindel

Univ Lyon, INSA Lyon

CREATIS, Inserm

Lyon, France

carole.frindel

@insa-lyon.fr

**Abstract**—Gait analysis is important for evaluating neuro-logical disorders such as stroke and Parkinson's disease. Traditionally, healthcare professionals had to rely on subjective assessments (i.e., human-based) of gait which were time consuming and not very reproducible. However, with the advent of IoT and indeed more objective (e.g., measurement-based) assessment methods, gait analysis can now be performed more accurately and effectively. It is worth noting, however, that there are still limitations to these objective methods, especially the lack of privacy-preserving continuous data collection. To overcome this limitation, we present in this paper a privacy-by-design monitoring application for post-stroke patients to evaluate their gait before, during, and after a rehabilitation program. Gait measurements are collected by a mobile application that continuously captures spatiotemporal parameters in the background using the built-in smartphone accelerometer. Statistical techniques are then applied to extract general indicators about the performed activity, as well as some more specific gait metrics in real-time such as regularity, symmetry and walking speed. These metrics are calculated based on the detected steps while patients are performing an activity. Additionally, a deep learning approach based on an auto-encoder is implemented to detect abnormal activities in the gait of patients. These analyses provide both valuable insights and statistical information about the activities performed by the patient, and a useful tool for practitioners to monitor the progression of neurological disorders and detect anomalies. We conducted experiments using this application in real conditions to monitor post-stroke patients in collaboration with a hospital, demonstrating its ability to compute valuable metrics and detect abnormal events patient's gait.

**Index Terms**—IoT, ML, privacy-by-design, mobile application, auto-encoder, anomaly detection, gait analysis, post-stroke rehabilitation

## I. INTRODUCTION

As the population ages, the prevalence of health issues is increasing, as indicated by previous studies [1], [2]. This trend has resulted in a growing demand for continuous monitoring and follow-up of patients. However, existing services face significant challenges, including substantial human resources, material requirements, and time constraints. Furthermore, achieving personalized monitoring for individual patients is often difficult, if not impossible. A viable solution is to automate and personalize the patient follow-up process by providing relevant metrics and indicators to both practitioner and patients while ensuring data privacy.

Recent studies have investigated the use of built-in smartphone sensors for various methods of gait analysis, including walking and step counting, based on time domain [3] and frequency domain [4]. Patient monitoring applications, with a focus on gait analysis, offer a valuable solution for evaluating neurological disorders. They empower practitioners to track the evolution of gait parameters before, during, and after rehabilitation programs. This capacity enables them to assess the effectiveness of interventions and make informed decisions regarding program development. However, concerns related to the intrusive nature of such monitoring and the potential breach of private life have limited the adoption of such solutions [5].

In this paper, we present a privacy-by-design monitoring application for post-stroke patients to evaluate their gait before, during, and after a rehabilitation program, along with the ability to detect gait anomalies. To achieve this, the mobile application continuously captures spatiotemporal parameters in the background using the built-in smartphone accelerometer. We employ both signal processing and deep learning approaches to extract general and specific indicators related to gait and the performed activity, as well as to detect abnormal activities in the gait of patients. To address privacy concerns, we ensure that raw data is processed exclusively on the user's device, with only specific gait indicators relevant to practitioners being transmitted to the application server. This minimisation scheme prevents the collection and centralisation of raw data, significantly reducing the risk of data breaches. Moreover, by sharing only a limited set of metrics and indicators, we substantially reduce the potential for inference attacks compared to using raw data [6], [7]. Our solution comprises a mobile application that collects information, calculates indicators, and presents metrics to patient through a customizable dashboard. Additionally, this mobile application securely transmits certain indicators to an application server, facilitating personalized patient monitoring by practitioners.

We conducted real-world experiments to monitor post-stroke patients in collaboration with an hospital. Our findings demonstrate that this mobile application can compute valuable gait metrics for both patients and practitioners, while minimizing battery consumption.
## II. POST-STROKE MONITORING APP

Our application has been meticulously designed to meet the requirements of a real-world medico-social use case with ARRPAC<fnref n="1" />. ARRPAC is a medico-social center part of the Hospices Civils de Lyon (HCL) that provides support for individuals dealing with post-stroke disorders. ARRPAC's mission is to address the medical and social needs of their patients. Our application, in this context, serves as a tool to enhance the personalization and efficacy of the support they offer. Through our partnering with ARRPAC, our objective is to demonstrate the potential of our solution in enhancing the delivery of medico-social services.

However, it is imperative to acknowledge that while there is a wealth of insights regarding the requirements of such a solution, we are confronted with several significant challenges. One of the primary challenges entails the necessity for continuous patient monitoring, which requires ongoing data processing. Consequently, we must develop sensor-probing strategies to minimize battery consumption. Another challenge involves preserving patient privacy, as the acceptance and adoption of these technologies depend on patients being assured of the security of their personal information. Furthermore, a flexible presentation dashboard is required for both practitioners and patients. This dashboard should be user-friendly, facilitating the clear and comprehensible visualization of important information.

The **architecture** of our privacy-preserving framework comprises three main elements: a client application running on the user's smartphone, the application server, and the hospital practitioner. In order to minimize the exposition of sensitive information, raw data remains exclusively on the smartphone and is not transmitted to the server. Instead, this data is processed locally on the smartphone, and only specific metrics and gait indicators are forwarded to the application server. This minimisation approach significantly reduces the potential for inference attacks that could be conducted by adversaries on stored data [6], [7] (i.e., from few metrics compared to raw data). In addition, patient metrics can be encrypted before being uploaded and stored in the server's database. Subsequently, the practitioner can request access to the application server (and decrypt the stored information, if encrypted) to monitor patients through a dashboard implemented as a website. This dashboard provides a clear and comprehensible representation of the patient results and progression.

**Data collection** is a continuous process that runs in the background with a lightweight approach to minimize its impact on battery consumption. To achieve that, the application initially collects data at a low sampling frequency until the detection of an activity triggers an increase in the sampling frequency. We provide two distinct categories of **metrics**. The first category pertains to physical indicators intended for patients and their families. These metrics encompass the monitoring of daily activities, including the count of steps and the classification of activity types as static or dynamic. The

second category focuses on measurements used to quantify different aspects of human gait, tailored for hospital practitioners engaged in gait analysis. These metrics encompass cadence, regularity, symmetry and step speed. To compute these metrics, we employ an autocorrelation function, which allows us to extract many essential gait parameters.

**Detection of abnormal gait activities in patients** is important, as these can often serve as early indicators of a deterioration in their condition. Promptly reporting this information to practitioners is crucial for providing better care. Anomalies in gait can manifest in various forms: 1) gait variability (stroke can result in motor control loss and reduced coordination), 2) altered gait timing (delays or early occurrences in different phases of the gait cycle) and 3) postural instability (struggle to maintain balance while walking). We train an auto-encoder locally on the smartphone to learn the patient's normal gait. Subsequently, we leverage this model to identify anomalies. Specifically, the trained model excels at accurately reconstructing signals associated with normal walking but struggles to correctly reconstruct abnormal data or data containing anomalies. Therefore, we employ the reconstruction error, equivalent to the training loss of the model, as an indicator for anomaly detection. To effectively identify anomalies, we introduce a threshold on the loss.

## III. CONCLUSION

The use of IoT-based objective methods has led to significant advancements in assessing neurological disorders. However, the field faces notable challenges related to privacy and computational limitations. In response, this paper introduces a novel solution – a privacy-focused monitoring application designed for post-stroke patients. This app utilizes smartphone accelerometers to gather spatiotemporal data and generate real-time gait metrics, offering valuable insights into patient monitoring and gait analysis. This has the potential to enhance the quality of care for stroke recovery patients.

## REFERENCES

[1] M. Yousufuddin and N. Young, "Aging and ischemic stroke," *Aging (Albany NY)*, vol. 11, no. 9, p. 2542, 2019.

[2] S. Dogra, D. W. Dunstan, T. Sugiyama, A. Stathi, P. A. Gardiner, and N. Owen, "Active aging and public health: Evidence, implications, and opportunities," *Annual review of public health*, vol. 43, pp. 439-459, 2022.

[3] D. Sankoff and J. B. Kruskal, "Time warps, string edits, and molecules: the theory and practice of sequence comparison," *Reading: Addison-Wesley Publication*, 1983.

[4] P. Rougé, A. Moukadem, A. Dieterlen, A. Boutet, and C. Frindel, "Generalizable features for anonymizing motion signals based on the zeros of the short-time fourier transform," *Journal of Signal Processing Systems*, vol. 95, no. 1, pp. 89-99, 2023.

[5] F. Schuster, "Security and privacy concerns for iot adoption in the home domain: A user perspective," 2022.

[6] T. Jourdan, A. Boutet, A. Bahi, and C. Frindel, "Privacy-Preserving IoT framework for activity recognition in personal healthcare monitoring," *ACM Transactions on Computing for Healthcare*, vol. 2, no. 1, pp. 1-22, Nov. 2020.

[7] A. Boutet, C. Frindel, S. Gambs, T. Jourdan, and R. C. Ngueveu, "DYSAN: Dynamically sanitizing motion sensor data against sensitive inferences through adversarial networks," in *ASIACCS*, 2021, pp. 672-686.

<footnote n="1">¹https://gcsms-arrpac.fr/</footnote>