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Massimo Canonico
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Canonico, M.; Desimoni, F.; Ferrero, A.; Grassi, P.A.; Irwin, C.; Campani, D.; Dal Molin, A.; Panella, M.; Magistrelli, L. Gait Monitoring and Analysis. Encyclopedia. Available online: https://encyclopedia.pub/entry/49535 (accessed on 03 July 2024).
Canonico M, Desimoni F, Ferrero A, Grassi PA, Irwin C, Campani D, et al. Gait Monitoring and Analysis. Encyclopedia. Available at: https://encyclopedia.pub/entry/49535. Accessed July 03, 2024.
Canonico, Massimo, Francesco Desimoni, Alberto Ferrero, Pietro Antonio Grassi, Christopher Irwin, Daiana Campani, Alberto Dal Molin, Massimiliano Panella, Luca Magistrelli. "Gait Monitoring and Analysis" Encyclopedia, https://encyclopedia.pub/entry/49535 (accessed July 03, 2024).
Canonico, M., Desimoni, F., Ferrero, A., Grassi, P.A., Irwin, C., Campani, D., Dal Molin, A., Panella, M., & Magistrelli, L. (2023, September 22). Gait Monitoring and Analysis. In Encyclopedia. https://encyclopedia.pub/entry/49535
Canonico, Massimo, et al. "Gait Monitoring and Analysis." Encyclopedia. Web. 22 September, 2023.
Gait Monitoring and Analysis
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This entry is adapted from the peer-reviewed paper 10.3390/s23187743

Gait abnormalities are common in the elderly and individuals diagnosed with Parkinson’s, often leading to reduced mobility and increased fall risk. Monitoring and assessing gait patterns in these populations play a crucial role in understanding disease progression, early detection of motor impairments, and developing personalized rehabilitation strategies. In particular, by identifying gait irregularities at an early stage, healthcare professionals can implement timely interventions and personalized therapeutic approaches, potentially delaying the onset of severe motor symptoms and improving overall patient outcomes.

cloud computing wearable devices gait monitoring telemedicine

1. Introduction

The world’s population is rapidly aging, and the number of older adults will increase significantly in the coming decades [1]. As individuals age, maintaining physical function and independence becomes crucial to their overall well-being. One key indicator of an individual’s physical function is their gait, which refers to the manner of walking or the pattern of movement during locomotion. Monitoring gait in older people holds significant importance as it can provide valuable insights into their overall health, risk of falls, and potential mobility limitations. Moreover, gait analysis serves as a noninvasive and cost-effective method to assess various age-related conditions and diseases affecting the musculoskeletal and neurological systems.
Gait analysis has long been recognized as a valuable tool in clinical research and rehabilitation settings [2]. By objectively evaluating gait parameters, such as stride length, gait speed, and cadence, healthcare professionals can gain critical insights into an individual’s mobility and balance. Monitoring gait patterns over time allows for detecting subtle changes that may signify early signs of decline or underlying health conditions. Such changes can range from minor alterations in walking speed to more pronounced abnormalities in gait rhythm or symmetry.
Falls represent a significant concern for older adults, as they can lead to severe injuries and loss of independence. Research indicates that gait abnormalities are often associated with an increased risk of falls among the elderly population [3][4]. By closely monitoring gait, healthcare providers can identify those at higher risk and implement appropriate interventions to prevent falls. Moreover, gait analysis can help identify specific gait parameters that may contribute to fall risk, enabling targeted interventions tailored to individual needs [5][6].
Furthermore, gait analysis provides insights into the functional decline associated with age-related musculoskeletal and neurological disorders. Conditions such as osteoarthritis, Parkinson’s disease, stroke, and peripheral neuropathy can significantly impact an individual’s gait pattern [7]. Continuous monitoring of gait parameters in these populations allows for the early detection of functional decline and facilitates the development of targeted interventions to mitigate the negative effects on mobility and overall quality of life.
In addition to diagnosing and managing specific conditions, monitoring gait in the elderly population can serve as a proactive approach to maintaining overall health and independence. By establishing baseline gait parameters for individuals, healthcare providers can track changes over time, enabling early interventions and personalized treatment plans.

2. Gait Monitoring and Analysis

In the last few years, there have been several studies aimed at analyzing the gait of people, in particular of the fragile ones.
In particular, a study proposed by the Politecnico di Torino [8] employs shoes with sensorized insoles and a chest-mounted device, both used to record data in a controlled laboratory setting. Other studies adopt video-based solutions [9] or different kinds of external systems, such as sensorized walkways [10]. Unfortunately, forcing a person to wear different devices or interact with unfamiliar technology to obtain accurate data from their gait could compromise the data themselves since the person can be uncomfortable and change the way he or she moves. Moreover, when a device makes the user uncomfortable, it can only be used for short continuous sessions, usually a few hours at best, thus entailing two main problems: to gather statistically meaningful amounts of data, several sessions need to take place, and short sessions are unlikely to feature relevant events such as stumbles and falls. The intensity of the symptoms caused by the disorders affecting the patients may vary during a given day, week, or year. Short and infrequent sessions could miss important fluctuations or key events and not fully represent the patient’s real condition.
Smaller and lighter devices can also create issues if the patient is not used to them and can only be used in a specific laboratory environment under qualified personnel supervision. In [11], the patients wore an inertial sensor attached to a waist-mounted strap situated on the lower back while performing a supervised timed up and go test to extract useful features from the signal for fall risk assessment. In [12], the authors placed a small accelerometer on the patients’ lower back before asking them to perform a series of tasks in a supervised laboratory environment to identify early markers of Parkinson’s disease (PD). Laboratory environments usually have smooth and flat surfaces without external influences, making these conditions significantly different from the ones people find in their daily lives, characterized by irregular surfaces and obstacles. The presence of other people can also make the patients feel anxious.
The data collected in these circumstances, for the reasons presented above, may not represent the real condition of the patients, who can be prone to alter their actions subconsciously and, thus, their gait. Many studies attempted to use more widespread and accessible devices, such as torso-mounted smartphones [13][14][15], which can accurately capture the forces involved in a walk with minimal noise from other sources. However, such devices still encounter low acceptance on behalf of the patients due to their weight, size, location, or stigmatization [16][17]. Smartwatches, like other wrist-worn devices and accessories, are significantly easier to wear, less bulky, and more likely to be accepted by the user. Unsurprisingly, the scientific literature has been moving in this direction for a few years. The goals in this scope are many and diverse.
In [18], the authors exploited the acceleration data available in the UK Biobank database to determine whether acceleration data collected during daily living can serve as a prodromal marker for PD. Although the number of subjects is extremely high, the authors only focused on generic information extracted from the data, such as average acceleration, without analyzing the patients’ gait. Another study using data from the same database is [19], in which the goal was to use free-living acceleration data to detect PD early. The authors split the raw data into segments of either gait or low acceleration magnitude and found notable differences between PD patients and healthy controls. Both studies only focused on PD without proposing a way to track a patient’s health status applicable to a more general population. Other studies, such as [20], also analyzed the gait for an entirely different reason. The authors wanted to extrapolate gait features from the acceleration data, which could authenticate the user wearing the device. While this study represents additional proof of how gait features can be observed at the wrist level, the data were collected by having subjects walk a corridor six times at three different paces, thus creating a controlled environment. Moreover, the study did not focus on the health status of the subjects or the quality of their gait.
A key aspect of gait analysis is differentiating what the user is doing, a task known as activity recognition. Studies such as [21][22] are useful in this scope. In the first one, the authors aimed at detecting gait segments in acceleration data collected in real-life scenarios. In contrast, the second one evaluates activity recognition and fall detection methods on different datasets. While understanding what the user is doing and isolating gait sections is the first step of assessing gait quality, the studies did not explore which features could be extracted from these sections and actions to obtain an approximate measure of the quality of the movements or the user’s health status.
In [23], the authors investigated the correlation between wrist movements and freezing events in patients with PD. This event detection is more closely related to health status than the two previous studies. However, the proposal is still too specific to be applied to a larger population. To create a reliable process to extrapolate features and detect gait events from acceleration data, the authors of [24] proposed a deep-learning-based automatic pipeline validated against a sensorized walkway. Although studies such as this one offer an interesting foundation, they do not explore how the features can be used to assess quality and health status.
Helpful information can be obtained by performing a spectral analysis of the data coming from body sensors. This is performed by applying the Fourier transform to the acceleration data. This procedure allows to compute some indicators, such as the mean value of the frequency, its variance, and its entropy. Having the data of single individuals over a pretty long period of time, the variation of those indicators can give information on the progress of the disease. 
In [25], the authors performed a Fourier analysis of the stride-by-stride estimates of the linear acceleration coming from body-worn inertial sensors. The forces measured by the triaxial accelerometer were transposed from a local to a global frame using a quaternion-based orientation estimation algorithm and by detecting when each stride began using a gait segmentation algorithm. The authors of [26] developed models for reducing the complexities of extracting features from data coming, among others, from wearable sensors. The extraction of the features is based on spectral analysis, and, in turn, the frequency analysis is based on the fast Fourier transform. Additionally, in [27], a method for analyzing data from a human body sensor was performed. It developed an algorithm that could identify the gait behavior with high precision. As in the previous ones, also in [27], the Fourier transform represents a fundamental tool in the classification process. Other applications of the Fourier transform for the analysis of frequencies can be found in [28], where the authors studied the data coming from people having different kinds of neuromuscular disorders. The authors of [28] developed software that could detect quantitative observations that could improve the subjects’ gait. For other details about the applications of the Fourier transform to human gait activity, see also the references contained in [25][26][27][28].

References

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  2. Studenski, S.; Perera, S.; Patel, K.V.; Rosano, C.; Faulkner, K.; Inzitari, M.; Brach, J.; Chandler, J.; Cawthon, P.; Connor, E.B. Gait speed and survival in older adults. JAMA 2011, 305, 50–58.
  3. Caristia, S.; Campani, D.; Cannici, C.; Frontera, E.; Giarda, G.; Pisterzi, S.; Terranova, L.; Payedimarri, A.B.; Faggiano, F.; Dal Molin, A. Physical exercise and fall prevention: A systematic review and meta-analysis of experimental studies included in Cochrane reviews. Geriatr. Nurs. 2021, 42, 1275–1286.
  4. Gambaro, E.; Gramaglia, C.; Azzolina, D.; Campani, D.; Molin, A.D.; Zeppegno, P. The complex associations between late life depression, fear of falling and risk of falls. A systematic review and meta-analysis. Ageing Res. Rev. 2022, 73, 101532.
  5. Campani, D.; Caristia, S.; Amariglio, A.; Piscone, S.; Ferrara, L.I.; Barisone, M.; Bortoluzzi, S.; Faggiano, F.; Dal Molin, A.; Group, I.W. Home and environmental hazards modification for fall prevention among the elderly. Public Health Nurs. 2021, 38, 493–501.
  6. Campani, D.; Caristia, S.; Amariglio, A.; Piscone, S.; Ferrara, L.I.; Bortoluzzi, S.; Faggiano, F.; Dal Molin, A.; Group, I.W. Effective, sustainable, and transferable physical exercise interventions for fall prevention among older people. Public Health Nurs. 2021, 38, 1140–1176.
  7. Campani, D.; De Luca, E.; Bassi, E.; Busca, E.; Airoldi, C.; Barisone, M.; Canonico, M.; Contaldi, E.; Capello, D.; De Marchi, F.; et al. The prevention of falls in patients with Parkinson’s disease with in-home monitoring using a wearable system: A pilot study protocol. Aging Clin. Exp. Res. 2022, 34, 3017–3024.
  8. Salis, F.; Bertuletti, S.; Bonci, T.; Caruso, M.; Scott, K.; Alcock, L.; Buckley, E.; Gazit, E.; Hansen, C.; Schwickert, L.; et al. A Multi-Sensor Wearable System for Gait Assessment in Real-World Conditions: Performance in Individuals with Impaired Mobility. 2023. Available online: https://www.researchsquare.com/article/rs-2486943/v1 (accessed on 28 August 2023).
  9. Ugbolue, U.C.; Papi, E.; Kaliarntas, K.T.; Kerr, A.; Earl, L.; Pomeroy, V.M.; Rowe, P.J. The evaluation of an inexpensive, 2D, video based gait assessment system for clinical use. Gait Posture 2013, 38, 483–489.
  10. Verghese, J.; Holtzer, R.; Lipton, R.B.; Wang, C. Quantitative gait markers and incident fall risk in older adults. J. Gerontol. Ser. A 2009, 64, 896–901.
  11. Lee, C.H.; Mendoza, T.; Huang, C.H.; Sun, T.L. Comparative analysis of fall risk assessment features in community-elderly and stroke survivors: Insights from sensor-based data. Healthcare 2023, 11, 1938.
  12. Del Din, S.; Elshehabi, M.; Galna, B.; Hobert, M.A.; Warmerdam, E.; Suenkel, U.; Brockmann, K.; Metzger, F.; Hansen, C.; Berg, D.; et al. Gait analysis with wearables predicts conversion to parkinson disease. Ann. Neurol. 2019, 86, 357–367.
  13. Isho, T.; Tashiro, H.; Usuda, S. Accelerometry-based gait characteristics evaluated using a smartphone and their association with fall risk in people with chronic stroke. J. Stroke Cerebrovasc. Dis. 2015, 24, 1305–1311.
  14. Nishiguchi, S.; Yamada, M.; Nagai, K.; Mori, S.; Kajiwara, Y.; Sonoda, T.; Yoshimura, K.; Yoshitomi, H.; Ito, H.; Okamoto, K.; et al. Reliability and validity of gait analysis by android-based smartphone. Telemed. e-Health 2012, 18, 292–296.
  15. Daines, K.J.; Baddour, N.; Burger, H.; Bavec, A.; Lemaire, E.D. Fall risk classification for people with lower extremity amputations using random forests and smartphone sensor features from a 6-minute walk test. PLoS ONE 2021, 16, e0247574.
  16. Yang, C.C.; Hsu, Y.L. A review of accelerometry-based wearable motion detectors for physical activity monitoring. Sensors 2010, 10, 7772–7788.
  17. Patel, S.; Park, H.; Bonato, P.; Chan, L.; Rodgers, M. A review of wearable sensors and systems with application in rehabilitation. J. Neuroeng. Rehabil. 2012, 9, 21.
  18. Schalkamp, A.K.; Peall, K.J.; Harrison, N.A.; Sandor, C. Wearable movement-tracking data identify Parkinson’s disease years before clinical diagnosis. Nat. Med. 2023, 29, 2048–2056.
  19. Williamson, J.R.; Telfer, B.; Mullany, R.; Friedl, K.E. Detecting Parkinson’s disease from wrist-worn accelerometry in the UK Biobank. Sensors 2021, 21, 2047.
  20. Cola, G.; Avvenuti, M.; Musso, F.; Vecchio, A. Gait-based authentication using a wrist-worn device. In Proceedings of the 13th International Conference on Mobile and Ubiquitous Systems: Computing, Networking and Services, Hiroshima, Japan, 28 November–1 December 2016; pp. 208–217.
  21. Soltani, A.; Paraschiv-Ionescu, A.; Dejnabadi, H.; Marques-Vidal, P.; Aminian, K. Real-world gait bout detection using a wrist sensor: An unsupervised real-life validation. IEEE Access 2020, 8, 102883–102896.
  22. Gjoreski, M.; Gjoreski, H.; Luštrek, M.; Gams, M. How accurately can your wrist device recognize daily activities and detect falls? Sensors 2016, 16, 800.
  23. Mazilu, S.; Blanke, U.; Calatroni, A.; Gazit, E.; Hausdorff, J.M.; Tröster, G. The role of wrist-mounted inertial sensors in detecting gait freeze episodes in Parkinson’s disease. Pervasive Mob. Comput. 2016, 33, 1–16.
  24. Peraza, L.R.; Kinnunen, K.M.; McNaney, R.; Craddock, I.J.; Whone, A.L.; Morgan, C.; Joules, R.; Wolz, R. An automatic gait analysis pipeline for wearable sensors: A pilot study in Parkinson’s disease. Sensors 2021, 21, 8286.
  25. Sabatini, A.M.; Ligorio, G.; Mannini, A. Fourier-based integration of quasi-periodic gait accelerations for drift-free displacement estimation using inertial sensors. BioMed. Eng. Online 2015, 14, 106.
  26. Teran-Pineda, D.; Thurnhofer-Hemsi, K.; Dominguez, E. Analysis and recognition of human gait activity based on multimodal sensors. Mathematic 2023, 11, 1538.
  27. Wang, X. A human body gait recognition system based on fourier transform and quartile difference extraction. Int. J. Online Eng. 2017, 13, 129–139.
  28. Ajani, A.S.; Ogunbode, O.A.; Adewumi, A.S.; Aremu, A.O.; Ajani, O.S.; Alade, M.O.; Jimoh, T.F. Development of digital gait monitoring software for diagnosis of neuromuscular disorder. Int. J. Biosens. Bioelectron. 2018, 4, 249.
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Subjects: Health Care Sciences & Services
Contributors MDPI registered users' name will be linked to their SciProfiles pages. To register with us, please refer to https://encyclopedia.pub/register :
Massimo Canonico
,
Francesco Desimoni
,
Alberto Ferrero
,
Pietro Antonio Grassi
,
Christopher Irwin
,
Daiana Campani
,
Alberto Dal Molin
,
Massimiliano Panella
,
Luca Magistrelli
View Times: 114
Revisions: 2 times (View History)
Update Date: 25 Sep 2023
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