We absolutely need objective measurements of our disabilities. Otherwise your therapists will never be able to assign workable therapies to a disability diagnosis.
Systematic review on the application of wearable inertial sensors to quantify everyday life motor activity in people with mobility impairments
Journal of NeuroEngineering and Rehabilitation volume 17, Article number: 148 (2020)
Abstract
Background
Recent advances in wearable sensor technologies enable objective and long-term monitoring of motor activities in a patient’s habitual environment. People with mobility impairments require appropriate data processing algorithms that deal with their altered movement patterns and determine clinically meaningful outcome measures. Over the years, a large variety of algorithms have been published and this review provides an overview of their outcome measures, the concepts of the algorithms, the type and placement of required sensors as well as the investigated patient populations and measurement properties.
Methods
A systematic search was conducted in MEDLINE, EMBASE, and SCOPUS in October 2019. The search strategy was designed to identify studies that (1) involved people with mobility impairments, (2) used wearable inertial sensors, (3) provided a description of the underlying algorithm, and (4) quantified an aspect of everyday life motor activity. The two review authors independently screened the search hits for eligibility and conducted the data extraction for the narrative review.
Results
Ninety-five studies were included in this review. They covered a large variety of outcome measures and algorithms which can be grouped into four categories: (1) maintaining and changing a body position, (2) walking and moving, (3) moving around using a wheelchair, and (4) activities that involve the upper extremity. The validity or reproducibility of these outcomes measures was investigated in fourteen different patient populations. Most of the studies evaluated the algorithm’s accuracy to detect certain activities in unlabeled raw data. The type and placement of required sensor technologies depends on the activity and outcome measure and are thoroughly described in this review. The usability of the applied sensor setups was rarely reported.
Conclusion
This systematic review provides a comprehensive overview of applications of wearable inertial sensors to quantify everyday life motor activity in people with mobility impairments. It summarizes the state-of-the-art, it provides quick access to the relevant literature, and it enables the identification of gaps for the evaluation of existing and the development of new algorithms.
Background
The protocol of this systematic review was published in advance [1], and the following introduction is an adapted and extended version of the introduction of that protocol.
People with mobility impairments may have difficulties in executing activities of daily living (activity limitations), or they may experience problems in involvement in life situations (participation restrictions) [2]. Rehabilitation services aim to improve these people’s abilities or make changes to their environment [3], to achieve a high level of independence and eventually increase the quality of life. Clinical assessments to estimate patients’ abilities and their rehabilitation progress are generally conducted in a standardized environment at a single time. Thus, they do not incorporate environmental and cognitive challenges of a patient’s habitual environment [4] and might be inaccurate when the symptoms of the patient fluctuate over time [5].
Recent advances in wearable sensor technologies enable objective and long-term monitoring of motor activities in a patient’s habitual environment. They provide an opportunity to overcome the aforementioned limitations of clinical assessments and complement their outcome measures. Accelerometers are the most commonly used wearable devices to quantify everyday life motor activity in clinical trials and clinical practice [6, 7]. Conventional outcome measures of accelerometers are activity counts as well as intensity levels and energy expenditure estimations based on cut-points of these counts [8]. These measures provide relevant information about whole-body physical activity, but they are non-specific and cannot determine movement patterns and types of activities performed [9]. In contrast, using a combination of several inertial sensors, such as accelerometers and gyroscopes, together with sophisticated data processing algorithms, allows estimating the quantity and other characteristics of everyday life motor activities [10]. Additional sensor technology such as magnetometers, barometers, wearable cameras, and heart rate monitors measure environmental factors or physiological responses to motor activities and can be combined with inertial sensors to gain further details about patients’ activities [11, 12]. Technological progress in the field of micro-electromechanical systems has made these devices small-sized, cost-effective, energy-efficient, and thus applicable for continuous long-term monitoring in unsupervised conditions [10]. However, continuous long-term monitoring generates a tremendous amount of unlabeled data that requires appropriate data processing algorithms to determine clinically meaningful outcome measures of everyday life motor activity. Typically, these algorithms detect a certain activity in unlabeled data as a first step (e.g., walking bouts or grasping an object) and then determine a measure to quantify the previously detected activity as a second step (e.g., walking speed or number of grasping activities).
The relevance of these outcome measures depends on end-users’ perspectives and may be different for people with mobility impairments compared to non-disabled individuals. For example, the amount of limping, use of assistive devices, and daily activity of affected limbs are more relevant to the former population. Altered movement patterns can also be a challenge for data processing algorithms [13, 14] and thus the transferability of algorithms which were evaluated in non-disabled individuals to people with mobility impairments could be limited. Therefore, this review focused on the application of inertial sensor technologies to quantify everyday life motor activity in people with mobility impairments and provides an overview of existing outcome measures as well as their underlying data processing algorithms. Specifically, the following research questions were addressed: (1) Which outcome measures have been used to quantify everyday life motor activity of people with mobility impairments under free-living conditions, and what are their corresponding data processing algorithms? (2) Which inertial sensor technology (accelerometer or gyroscope), possibly in combination with additional wearable sensor technology, is required to assess these measures? (3) Where need inertial sensors be placed to assess these measures and minimally restrict activities of daily living? (4) In which patient populations were these measures applied, and were they and the required sensor system evaluated in terms of validity, reproducibility, or usability?
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