Researchers from MIT and elsewhere demonstrated an in-home wireless device that can monitor a patient’s movement and gait speed, which can be used to evaluate Parkinson’s severity, the progression of the disease, and the patient’s response to medication.
The device, which is about the size of a Wi-Fi router, gathers data passively using radio signals that reflect off the patient’s body as they move around their home. The patient does not need to wear a gadget or change their behavior. (A recent study, for example, showed that this type of device could be used to detect Parkinson’s from a person’s breathing patterns while sleeping.)
The researchers used these devices to conduct two studies that involved a total of 50 participants. They showed that, by using machine-learning algorithms to analyze the troves of data they gathered (more than 200,000 gait speed measurements), a clinician could track Parkinson’s progression more effectively than they would with periodic, in-clinic evaluations.
“By being able to have a device in the home that can monitor a patient and tell the doctor remotely about the progression of the disease, and the patient’s medication response so they can attend to the patient even if the patient can’t come to the clinic — now they have real, reliable information — that actually goes a long way toward improving equity and access,” says senior author Dina Katabi, the Thuan and Nicole Pham Professor in the Department of Electrical Engineering and Computer Science (EECS), and a principle investigator in the Computer Science and Artificial Intelligence Laboratory (CSAIL) and the MIT Jameel Clinic.
The co-lead authors are EECS graduate students Yingcheng Liu and Guo Zhang. The research is published in Science Translational Medicine.
A Human Radar
This work utilizes a wireless device previously developed in the Katabi lab that analyzes radio signals that bounce off people’s bodies. It transmits signals that use a tiny fraction of the power of a Wi-Fi router — these super-low-power signals don’t interfere with other wireless devices in the home. While radio signals pass through walls and other solid objects, they are reflected off humans due to the water in our bodies.
This creates a “human radar” that can track the movement of a person in a room. Radio waves always travel at the same speed, so the length of time it takes the signals to reflect back to the device indicates how the person is moving.
The device incorporates a machine-learning classifier that can pick out the precise radio signals reflected off the patient even when there are other people moving around the room. Sophisticated algorithms use these movement data to compute gait speed — how fast the person is walking.
Because the wireless device operates in the background and runs all day, every day, it can collect a massive amount of data. The researchers wanted to see if they could apply machine learning to these datasets to gain insights about the disease over time.
They gathered 50 participants, 34 of whom had Parkinson’s, and conducted two observational studies of in-home gait measurements. One study lasted two months and the other was conducted over the course of two years. Through the studies, the researchers collected more than 200,000 individual measurements that they averaged to smooth out variability due to the condition of the device or other factors. (For example, the device might accidentally get switched off during cleaning, or a patient may walk more slowly when talking on the phone.)
They used statistical methods to analyze the data and found that in-home gait speed can be used to effectively track Parkinson’s progression and severity. For instance, they showed that gait speed declined almost twice as fast for individuals with Parkinson’s, compared to those without.
“Monitoring the patient continuously as they move around the room enabled us to get really good measurements of their gait speed. And with so much data, we were able to perform aggregation that allowed us to see very small differences,” Zhang says.
Better, Faster Results
Drilling down on these variabilities offered some key insights. For instance, the researchers could see that intraday fluctuations in a patient’s gait speed correspond with how they are responding to their medication — gait speed may improve after a dose and then begin to decline after a period of time.
“This really gives us the possibility to objectively measure how your mobility responds to your medication. Previously, this was nearly impossible to do because this medication effect could only be measured by having the patient keep a journal,” Liu says.
A clinician could use these data to adjust medication dosage more effectively and accurately. This is especially important since many drugs used to treat disease symptoms can cause serious side effects if the patient receives too much.
The researchers were able to demonstrate statistically significant results regarding Parkinson’s progression after studying 50 people for just one year; by contrast, an often-cited study by the Michael J. Fox Foundation involved over 500 individuals and monitored them for more than five years, Katabi says.
“For a drug company or a biotech company trying to develop medicines for this disease, this could greatly reduce the burden and cost and speed up the development of new therapies,” she adds.
Katabi credits much of the study’s success to the dedicated team of scientists and clinicians who worked together to tackle the many difficulties that arose along the way. For one, they began the study before the Covid-19 pandemic, so engineers initially entered people’s homes to set up the devices. When that was no longer possible, they developed a method to remotely deploy devices and created a user-friendly app for participants and clinicians.
Through the course of the study, they learned to automate processes and reduce effort, especially for the participants and clinical team.
This knowledge will prove useful as they look to deploy devices in at-home studies of other neurological disorders, such as Alzheimer’s, ALS, and Huntington’s. They also want to explore how these methods could be used, in conjunction with other work from the Katabi lab showing that Parkinson’s can be diagnosed by monitoring breathing, to collect a holistic set of markers that could diagnose the disease early and then be used to track and treat it.
This work is supported, in part, by the National Institutes of Health and the Michael J. Fox Foundation.
[Source(s): Massachusetts Institute of Technology, EurekAlert]
Watafiti kutoka MIT na mahali pengine walionyesha kifaa cha wireless cha nyumbani ambacho kinaweza kufuatilia mwendo wa mgonjwa na kasi ya kutembea, ambayo inaweza kutumika kutathmini ukali wa Parkinson, maendeleo ya ugonjwa, na majibu ya mgonjwa kwa dawa. Kifaa hicho, ambacho kina ukubwa wa router ya Wi-Fi, hukusanya data kwa kutumia ishara za redio ambazo huonyeshwa kwenye mwili wa mgonjwa wanapozunguka nyumbani. Mgonjwa hahitaji kuvaa kifaa au kubadilisha tabia yake. (Kwa mfano, uchunguzi wa hivi karibuni ulionyesha kwamba kifaa cha aina hiyo chaweza kutumiwa kugundua ugonjwa wa Parkinson kwa kuchunguza jinsi mtu anavyopumua anapolala.) Watafiti walitumia vifaa hivyo kufanya uchunguzi wa mara mbili ambao ulihusisha washiriki 50. Kwa kutumia algorithms za kujifunza mashine kuchambua data zilizokusanywa (zaidi ya vipimo vya kasi ya kutembea 200,000), daktari anaweza kufuatilia maendeleo ya Parkinson kwa ufanisi zaidi kuliko wangefanya na tathmini za mara kwa mara za kliniki. "Kwa kuwa na uwezo wa kuwa na kifaa nyumbani ambacho kinaweza kumfuatilia mgonjwa na kumwambia daktari kwa mbali juu ya maendeleo ya ugonjwa, na majibu ya dawa ya mgonjwa ili waweze kuhudhuria mgonjwa hata ikiwa mgonjwa hawezi kuja kwenye kliniki, sasa wana habari halisi, ya kuaminika ambayo kwa kweli inaendelea kwa muda mrefu kuelekea kuboresha usawa na upatikanaji, ""anasema mwandishi mwandamizi Nicole Katabi, Profesa wa Nguvu na Sayansi ya Kompyuta (EECS) na Mtaalamu wa Sayansi ya Kompyuta na Akili ya Ufundi (CSAIL)." Waandishi wa utafiti huo ni wanafunzi wa shahada ya kwanza wa EECS, Ying Cheng-Liu na Guo Zhang. Utafiti huo umechapishwa katika jarida la Science Translational Medicine. Radar ya Binadamu: Kazi hii hutumia kifaa cha wireless kilichotengenezwa hapo awali katika maabara ya Katabi ambacho kinachambua ishara za redio ambazo hupiga mwili wa watu. Inatuma ishara ambazo hutumia sehemu ndogo ya nguvu ya router ya Wi-Fi - ishara hizi za nguvu ya chini sana hazisumbui vifaa vingine vya wireless nyumbani. Wakati ishara za redio hupita kupitia kuta na vitu vingine vyenye nguvu, zinaonyeshwa kutoka kwa wanadamu kwa sababu ya maji katika miili yetu. Hii huunda "radar ya kibinadamu" ambayo inaweza kufuatilia harakati ya mtu katika chumba. Mawimbi ya redio husafiri kwa kasi sawa, kwa hiyo muda unaotumiwa kutafakari ishara hizo kwenye kifaa huonyesha jinsi mtu anavyohama. Kifaa hicho kina classifier ya kujifunza mashine ambayo inaweza kuchagua ishara za redio sahihi zilizotajwa kutoka kwa mgonjwa hata wakati kuna watu wengine wakizunguka chumba. Algorithms ya kisasa hutumia data hizi za mwendo kuhesabu kasi ya kutembea <unk> jinsi mtu anavyoenda haraka. Kwa sababu kifaa cha wireless inafanya kazi katika background na anaendesha siku nzima, kila siku, inaweza kukusanya kiasi kikubwa cha data. Watafiti walitaka kuona ikiwa wangeweza kutumia kujifunza mashine kwa seti hizi za data kupata ufahamu juu ya ugonjwa huo kwa muda. Watafiti hao waliwakusanya washiriki 50 ambao 34 walikuwa na ugonjwa wa Parkinson na wakafanya uchunguzi wa mara mbili wa kutembea kwa watu nyumbani. Uchunguzi mmoja ulidumu miezi miwili na ule mwingine ulifanywa kwa muda wa miaka miwili. Katika utafiti huo, watafiti walikusanya zaidi ya vipimo 200,000 vya mtu binafsi ambavyo walifanya wastani ili kupunguza kutofautiana kwa sababu ya hali ya kifaa au sababu zingine. (Kwa mfano, kifaa hicho kingeweza kuzimwa kimakosa wakati wa kusafisha, au mgonjwa angeweza kutembea polepole zaidi anapozungumza kwa simu.) Walitumia mbinu za takwimu kuchambua data na kugundua kwamba kasi ya kutembea nyumbani inaweza kutumika kwa ufanisi kufuatilia maendeleo na ukali wa Parkinson. Kwa mfano, walionyesha kwamba kasi ya kutembea ilipungua karibu mara mbili kwa kasi kwa watu wenye Parkinson, ikilinganishwa na wale wasio na ugonjwa huo. <unk>Kufuatilia mgonjwa daima wanapozunguka chumba kulituwezesha kupata vipimo vizuri sana vya kasi ya kutembea kwao. Na kwa data nyingi sana, tuliweza kufanya mkusanyiko ambao ulituwezesha kuona tofauti ndogo sana, "Zhang anasema. Bora, Matokeo ya Haraka Drilling chini juu ya tofauti hizi alitoa baadhi ya ufahamu muhimu. Kwa mfano, watafiti waliweza kuona kwamba mabadiliko ya ndani ya siku katika kasi ya kutembea ya mgonjwa inalingana na jinsi wanavyojibu dawa zao - kasi ya kutembea inaweza kuboresha baada ya kipimo na kisha kuanza kupungua baada ya kipindi cha muda. <unk>Hii kweli inatupa uwezekano wa kupima objectively jinsi mwendo wako anajibu kwa dawa yako. Hapo awali, hii ilikuwa karibu haiwezekani kufanya kwa sababu athari hii ya dawa inaweza tu kupimwa kwa kuwa na mgonjwa kuweka jarida, <unk> Liu anasema. Daktari angeweza kutumia data hizo kurekebisha kipimo cha dawa kwa njia yenye matokeo zaidi na sahihi zaidi. Hii ni muhimu hasa kwa sababu dawa nyingi zinazotumiwa kutibu dalili za ugonjwa zinaweza kusababisha athari mbaya za upande ikiwa mgonjwa anapokea kiasi kikubwa sana. Watafiti waliweza kuonyesha matokeo muhimu ya takwimu kuhusu maendeleo ya Parkinson baada ya kusoma watu 50 kwa mwaka mmoja tu; kwa kulinganisha, utafiti uliotajwa mara nyingi na Michael J. "Fundation ya Fox ilihusisha watu zaidi ya 500 na kuwafuatilia kwa zaidi ya miaka mitano,"" Katabi anasema." "Kwa kampuni ya dawa au kampuni ya biotech kujaribu kuendeleza dawa kwa ajili ya ugonjwa huu, hii inaweza sana kupunguza mzigo na gharama na kuharakisha maendeleo ya matibabu mapya, "anaongeza. Katabi anaamini mafanikio ya utafiti huo yalitokana na timu ya wanasayansi na madaktari waliofanya kazi pamoja ili kukabiliana na matatizo mengi yaliyotokea. Kwa mfano, utafiti huo ulianza kabla ya janga la COVID-19, kwa hivyo wahandisi waliingia nyumbani kwa watu ili kuanzisha vifaa. Wakati hiyo haikuwezekana tena, waliendeleza njia ya kupeleka vifaa kwa mbali na kuunda programu ya kirafiki kwa washiriki na madaktari. Katika kipindi cha utafiti, walijifunza jinsi ya kuendesha mchakato na kupunguza juhudi, hasa kwa washiriki na timu ya kliniki. Ujuzi huu utathibitika kuwa muhimu wakati wanatafuta kupeleka vifaa katika masomo ya nyumbani ya magonjwa mengine ya neva, kama vile Alzheimer, ALS, na Huntington. Pia wanataka kuchunguza jinsi mbinu hizi zinaweza kutumika, pamoja na kazi nyingine kutoka kwa maabara ya Katabi inayoonyesha kwamba Parkinson inaweza kugunduliwa kwa kufuatilia kupumua, kukusanya seti ya alama za jumla ambazo zinaweza kugundua ugonjwa mapema na kisha kutumika kufuatilia na kutibu. Kazi hii inasaidiwa, kwa sehemu, na Taasisi za Kitaifa za Afya na Michael J. Msingi wa Fox. [Chanzo: Taasisi ya Teknolojia ya Massachusetts, EurekAlert]