Smartphone Motion Sensors and Healthcare: On the Cusp of a Disruption?

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Vinay Prabhu, Principal Machine Learning Scientist at UnifyID Inc
Vinay Prabhu, Principal Machine Learning Scientist at UnifyID Inc

On a balmy mid-April afternoon, amidst the cacophony of passionate deliberations ringing the walls of the Paul Brest Hall on Stanford University’s sylvan green campus, a picture began to emerge. It was the annual mHealth Connect conference and most of the talks by academic stalwarts, healthcare entrepreneurs and venture capitalists were harping on a common thesis: The growing nexus between mobile sensors and AI (Artificial Intelligence) in the healthcare domain.

Amidst ardent calls for silicon-valley styled disruption of the ailing healthcare sector, it became ostensibly clear that three facets of personal healthcare were going to see important changes driven by the emergence of the sensors-laden smartphone as the centerpiece of democratization of healthcare-hardware.

In the next few paragraphs, the reader will be exposed to the following three domains earmarked to be recipients of the disruption:

1: Gait and Posture related disorders.
2: Geriatric Care
3: Treatment of neurodegenerative disorders

On first glance, it might seem as if ushering these advancements demands dramatic popularization of highly specialized and potential expensive medical sensors such as the Photo-Plethysmo-Gram (PPG) heart rate monitor sensor found thus far only on high-end phones such as Samsung S8. Far from it, we will see that many solutions catering to a wide array of challenges in all three of the above-mentioned domains, will, in fact, be fueled by data emanating from the humble triad of Inertial Measurement Unit-IMU-sensors, or motion sensors, that is accelerometers, gyroscopes and magnetometers.

With this background, let us dive deeper into the specific domains and the success stories that have emerged.

1. Gait and Posture related disorders

Paleoanthropologists believe that approximately 4 to 2.8 million years ago, the now extinct hominin Australopithecus afarensis, had finally mastered the art of performing a series of complicated, cooperative and coordinated manipulation of the musculoskeletal and locomotor controller systems, resulting in a physical activity that we now take for granted: The upright human bipedal walk!

The repetitive pattern spanning two successive steps or one complete stride constitutes what is formally studied as a Gait cycle. A gait cycle begins when the heel of one foot strikes the ground and culminates the instant when the same foot makes contact with the ground again. As per the RLA (Rancho Los Amigos) system, a single gait cycle consists of eight temporal phases, which include: Initial Contact, Loading Response, Mid-stance, Terminal Stance, Pre-swing, Initial Swing, Mid Swing and Late Swing phase.

Fine grained analysis of gait reveals an astonishingly wide array of clues about the person’s state of general health, both from the orthopedic perspective as well as the neuromuscular perspective. From an orthopedic viewpoint, study of gait kinematic deviations from the normative model is an integral part of diagnosis of conditions such as developmental dysplasia of the hip (DDH) , Hip fracture and chronic ankle instability. The Stanford Motion and gait-analysis laboratory lists eight basic pathological gaits that can be attributed to neurological conditions: hemiplegic, spastic diplegic, neuropathic, myopathic, Parkinsonian, choreiform, ataxic (cerebellar) and sensory.

A typical gait analysis study entails a Physical exam, followed by video analysis and possibly Electromyographic (EMG) measurements of muscle activity during movement. Enter the scene, motion sensors. Modern day smart-phone sensors such as Accelerometers and Gyroscopes that allow sampling rates of as high as 400 samples/sec facilitate highly precise measurements of the gait cycle that can be done by the patient just placing the phone in his/her pocket and walking while at the comfort of one’s home.

In Figure 1, we see a visualization of gait cycle time-series signals measured from the accelerometer of a smartphone carried by a volunteer at UnifyID labs.

 Visualization of multi-session accelerometric gait cycle measurements from a volunteer
Figure 1: Visualization of multi-session accelerometric gait cycle measurements from a volunteer

As for the question of these measurements meeting the rigor of a proper medical examination, there exists a plethora of peer-reviewed published research that showcases that it is indeed the case. Nearly eight years ago, Rigoberto et al [1] presented a study that showcased how they had successfully used an off-the-shelf commercial smartphone as a tool to evaluate anticipatory postural adjustments (APA) before the beginning of normal gait in healthy subjects. In the same year, Lemoyne et al [2] demonstrated an implementation of an iPhone as a wireless accelerometer for rigorously quantifying gait characteristics. Then in 2012, Nishiguchi et al [3] studied in detail the reliability and validity of gait analysis by the Xperia SO-01B Android smartphone on 30 subjects and concluded that the smartphone-based measurements indeed had the capacity to quantify gait parameters with a comparable degree of accuracy that was attained using a dedicated medical grade tri-axial accelerometeric sensor. In 2016, we saw the first usage of a smartphone as a wireless gyroscopic platform for quantifying reduced arm swing in hemiplegie gait [4]. More recently, researchers from Toho university [5] have also developed the Gait-Kun system consisting of tri-axial accelerometers and shown promising levels of accuracy in measuring ataxia due to spinocerebellar degeneration.

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